DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction.

Analyzing the spatial organization of molecules in cells and tissues is a cornerstone of biological research and clinical practice. However, despite enormous progress in molecular profiling of cellular constituents, spatially mapping them remains a disjointed and specialized machinery-intensive process, relying on either light microscopy or direct physical registration. Here, we demonstrate DNA microscopy, a distinct imaging modality for scalable, optics-free mapping of relative biomolecule positions. In DNA microscopy of transcripts, transcript molecules are tagged in situ with randomized nucleotides, labeling each molecule uniquely. A second in situ reaction then amplifies the tagged molecules, concatenates the resulting copies, and adds new randomized nucleotides to uniquely label each concatenation event. An algorithm decodes molecular proximities from these concatenated sequences and infers physical images of the original transcripts at cellular resolution with precise sequence information. Because its imaging power derives entirely from diffusive molecular dynamics, DNA microscopy constitutes a chemically encoded microscopy system.

Variants in ZFX are associated with an X-linked neurodevelopmental disorder with recurrent facial gestalt.

Pathogenic variants in multiple genes on the X chromosome have been implicated in syndromic and non-syndromic intellectual disability disorders. ZFX on Xp22.11 encodes a transcription factor that has been linked to diverse processes including oncogenesis and development, but germline variants have not been characterized in association with disease. Here, we present clinical and molecular characterization of 18 individuals with germline ZFX variants. Exome or genome sequencing revealed 11 variants in 18 subjects (14 males and 4 females) from 16 unrelated families. Four missense variants were identified in 11 subjects, with seven truncation variants in the remaining individuals. Clinical findings included developmental delay/intellectual disability, behavioral abnormalities, hypotonia, and congenital anomalies. Overlapping and recurrent facial features were identified in all subjects, including thickening and medial broadening of eyebrows, variations in the shape of the face, external eye abnormalities, smooth and/or long philtrum, and ear abnormalities. Hyperparathyroidism was found in four families with missense variants, and enrichment of different tumor types was observed. In molecular studies, DNA-binding domain variants elicited differential expression of a small set of target genes relative to wild-type ZFX in cultured cells, suggesting a gain or loss of transcriptional activity. Additionally, a zebrafish model of ZFX loss displayed an altered behavioral phenotype, providing additional evidence for the functional significance of ZFX. Our clinical and experimental data support that variants in ZFX are associated with an X-linked intellectual disability syndrome characterized by a recurrent facial gestalt, neurocognitive and behavioral abnormalities, and an increased risk for congenital anomalies and hyperparathyroidism.

Stable diverse food webs become more common when interactions are more biologically constrained.

Ecologists have long sought to understand how diversity and structure mediate the stability of whole ecosystems. For high-diversity food webs, the interactions between species are typically represented using matrices with randomly chosen interaction strengths. Unfortunately, this procedure tends to produce ecological systems with no underlying equilibrium solution, and so ecological inferences from this approach may be biased by nonbiological outcomes. Using recent computationally efficient methodological advances from metabolic networks, we employ for the first time an inverse approach to diversity-stability research. We compare classical random interaction matrices of realistic food web topology (hereafter the classical model) to feasible, biologically constrained, webs produced using the inverse approach. We show that an energetically constrained feasible model yields a far higher proportion of stable high-diversity webs than the classical random matrix approach. When we examine the energetically constrained interaction strength distributions of these matrix models, we find that although these diverse webs have consistent negative self-regulation, they do not require strong self-regulation to persist. These energetically constrained diverse webs instead show an increasing preponderance of weak interactions that are known to increase local stability. Further examination shows that some of these weak interactions naturally appear to arise in the model food webs from a constraint-generated realistic generalist-specialist trade-off, whereby generalist predators have weaker interactions than more specialized species. Additionally, the inverse technique we present here has enormous promise for understanding the role of the biological structure behind stable high-diversity webs and for linking empirical data to the theory.

Sensing prior constraints in deep neural networks for solving exploration geophysical problems.

One of the key objectives in geophysics is to characterize the subsurface through the process of analyzing and interpreting geophysical field data that are typically acquired at the surface. Data-driven deep learning methods have enormous potential for accelerating and simplifying the process but also face many challenges, including poor generalizability, weak interpretability, and physical inconsistency. We present three strategies for imposing domain knowledge constraints on deep neural networks (DNNs) to help address these challenges. The first strategy is to integrate constraints into data by generating synthetic training datasets through geological and geophysical forward modeling and properly encoding prior knowledge as part of the input fed into the DNNs. The second strategy is to design nontrainable custom layers of physical operators and preconditioners in the DNN architecture to modify or shape feature maps calculated within the network to make them consistent with the prior knowledge. The final strategy is to implement prior geological information and geophysical laws as regularization terms in loss functions for training the DNNs. We discuss the implementation of these strategies in detail and demonstrate their effectiveness by applying them to geophysical data processing, imaging, interpretation, and subsurface model building.

Elliptic PDE learning is provably data-efficient.

Partial differential equations (PDE) learning is an emerging field that combines physics and machine learning to recover unknown physical systems from experimental data. While deep learning models traditionally require copious amounts of training data, recent PDE learning techniques achieve spectacular results with limited data availability. Still, these results are empirical. Our work provides theoretical guarantees on the number of input-output training pairs required in PDE learning. Specifically, we exploit randomized numerical linear algebra and PDE theory to derive a provably data-efficient algorithm that recovers solution operators of three-dimensional uniformly elliptic PDEs from input-output data and achieves an exponential convergence rate of the error with respect to the size of the training dataset with an exceptionally high probability of success.

Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learning.

Following their success in numerous imaging and computer vision applications, deep-learning (DL) techniques have emerged as one of the most prominent strategies for accelerated MRI reconstruction. These methods have been shown to outperform conventional regularized methods based on compressed sensing (CS). However, in most comparisons, CS is implemented with two or three hand-tuned parameters, while DL methods enjoy a plethora of advanced data science tools. In this work, we revisit [Formula: see text]-wavelet CS reconstruction using these modern tools. Using ideas such as algorithm unrolling and advanced optimization methods over large databases that DL algorithms utilize, along with conventional insights from wavelet representations and CS theory, we show that [Formula: see text]-wavelet CS can be fine-tuned to a level close to DL reconstruction for accelerated MRI. The optimized [Formula: see text]-wavelet CS method uses only 128 parameters compared to >500,000 for DL, employs a convex reconstruction at inference time, and performs within <1% of a DL approach that has been used in multiple studies in terms of quantitative quality metrics.

The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem.

Deep learning (DL) has had unprecedented success and is now entering scientific computing with full force. However, current DL methods typically suffer from instability, even when universal approximation properties guarantee the existence of stable neural networks (NNs). We address this paradox by demonstrating basic well-conditioned problems in scientific computing where one can prove the existence of NNs with great approximation qualities; however, there does not exist any algorithm, even randomized, that can train (or compute) such a NN. For any positive integers K>2 and L, there are cases where simultaneously 1) no randomized training algorithm can compute a NN correct to K digits with probability greater than 1/2; 2) there exists a deterministic training algorithm that computes a NN with K ­1 correct digits, but any such (even randomized) algorithm needs arbitrarily many training data; and 3) there exists a deterministic training algorithm that computes a NN with K ­2 correct digits using no more than L training samples. These results imply a classification theory describing conditions under which (stable) NNs with a given accuracy can be computed by an algorithm. We begin this theory by establishing sufficient conditions for the existence of algorithms that compute stable NNs in inverse problems. We introduce fast iterative restarted networks (FIRENETs), which we both prove and numerically verify are stable. Moreover, we prove that only O(|log (ϵ)|) layers are needed for an ϵ-accurate solution to the inverse problem.

Different concepts of neighborhood safety and child internalizing and externalizing behaviors.

Neighborhood safety is defined inconsistently across epidemiologic studies - a conceptual problem that results in incomparable measurements, hampering the design of health interventions. Using child behavior problems (measured via the Child Behavior Checklist) as the outcome of interest, this study directly compared four measures of neighborhood safety: two of experienced safety and two of perceived safety, with each one measured at family and community levels. These included children's direct experience of harm, parental perceptions, community crime statistics, and community perceptions. In a sample of 3291 ten-year-olds from the Generation R cohort (living in municipal Rotterdam, Netherlands, 2013), all four measures were correlated (χ2 ≥ 9.2, P < 0.002 in pairwise chi-square comparisons), but ultimately identified different levels of risk for behavioral health. Direct experiences of harm, parental perceptions, and community crime statistics were all associated with increased child internalizing behaviors (ß = 3.12, ß = 2.10, and ß = 1.77, respectively), while only experiences of harm and parental perceptions were associated with increased externalizing behaviors (ß = 2.75 and ß = 1.31, respectively). These results provide novel evidence that the conceptual distinctions underlying different measures of neighborhood safety are meaningful for child mental health and should be considered in intervention design.

Attention-deficit hyperactivity disorder symptoms and brain morphology: Addressing potential selection bias with inverse probability weighting.

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9-11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.

Impact of protein conformations on binding free energy calculations in the beta-secretase 1 system.

In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2 kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.

Self- and interviewer-reported cognitive problems in relation to cognitive decline and dementia: results from two prospective studies.

BACKGROUND: Little is known regarding the association of interviewer-reported cognitive problems (ICP) with age-related cognitive decline. We aimed to investigate the independent associations of ICP and the combined associations of ICP and self-reported cognitive problems (SCP) with subsequent cognitive decline and dementia in two prospective cohort studies. METHODS: We included 10,976 Chinese (age = 57.7 ± 8.7) and 40,499 European (age = 64.6 ± 9.4) adults without dementia from the China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE). Self-rated memory (5-point scale) and interviewer-rated frequencies of asking for clarification (6-point scale) were used to define SCP and ICP (dichotomized). Outcomes included objective cognitive test scores (z-score transformation) and incident dementia. Generalized estimating equation models were performed to evaluate mean differences in objective cognitive decline. Logistic and Cox regression models were used to estimate the relative risk of dementia. Results from two cohorts were pooled using the random-effects models. RESULTS: ICP was associated with faster cognitive decline in CHARLS (ßCHARLS = -0.025 [-0.044, -0.006] z-score/year). ICP and SCP were also independently associated with higher risk of dementia in two cohorts (pooled relative risk for SCP = 1.73 [1.30, 2.29]; pooled relative risk for ICP = 1.40 [1.10, 1.79]). In the joint analysis, participants with coexistence of SCP and ICP had the fastest cognitive decline (ßCHARLS = -0.051 [-0.080, -0.021]; ßSHARE = -0.024 [-0.043, -0.004]; pooled ß = -0.035 [-0.061, -0.009] z-score/year) and highest risk of dementia (ORCHARLS = 1.77 [1.42, 2.20]; HRSHARE = 2.94 [2.42, 3.59]; pooled relative risk = 2.29 [1.38, 3.77]). CONCLUSIONS: The study suggested that interviewer-reported cognitive problems may be early indicators of cognitive decline and dementia in middle-aged and older adults. A combination of self- and interviewer-reported cognitive problems showed the strongest associations with cognitive decline and dementia.

Development of prescribing indicators related to opioid-related harm in patients with chronic pain in primary care-a modified e-Delphi study.

BACKGROUND: Long-term opioid use is associated with dependency, addiction, and serious adverse events. Although a framework to reduce inappropriate opioid prescribing exists, there is no consensus on prescribing indicators for preventable opioid-related problems in patients with chronic pain in primary care in the UK. This study aimed to identify opioid prescription scenarios for developing indicators for prescribing opioids to patients with chronic pain in primary care. METHODS: Scenarios of opioid prescribing indicators were identified from a literature review, guidelines, and government reports. Twenty-one indicators were identified and presented in various opioid scenarios concerning opioid-related harm and adverse effects, drug-drug interactions, and drug-disease interactions in certain disease conditions. After receiving ethics approval, two rounds of electronic Delphi panel technique surveys were conducted with 24 expert panellists from the UK (clinicians, pharmacists, and independent prescribers) from August 2020 to February 2021. Each indicator was rated on a 1-9 scale from inappropriate to appropriate. The score's median, 30th and 70th percentiles, and disagreement index were calculated. RESULTS: The panel unanimously agreed that 15 out of the 21 opioid prescribing scenarios were inappropriate, primarily due to their potential for causing harm to patients. This consensus was reflected in the low appropriateness scores (median ranging from 1 to 3). There were no scenarios with a high consensus that prescribing was appropriate. The indicators were considered inappropriate due to drug-disease interactions (n = 8), drug-drug interactions (n = 2), adverse effects (n = 3), and prescribed dose and duration (n = 2). Examples included prescribing opioids during pregnancy, concurrently with benzodiazepines, long-term without a laxative prescription and prescribing > 120-mg morphine milligram equivalent per day or long-term duration over 3 months after surgery. CONCLUSIONS: The high agreement on opioid prescribing indicators indicates that these potentially hazardous consequences are relevant and concerning to healthcare practitioners. Future research is needed to evaluate the feasibility and implementation of these indicators within primary care settings. This research will provide valuable insights and evidence to support opioid prescribing and deprescribing strategies. Moreover, the findings will be crucial in informing primary care practitioners and shaping quality outcome frameworks and other initiatives to enhance the safety and quality of care in primary care settings.

Additives for Aqueous Zinc-Ion Batteries: Recent Progress, Mechanism Analysis, and Future Perspectives.

Aqueous zinc-ion batteries (ZIBs) stand out as a promising next-generation electrochemical energy storage technology, offering notable advantages such as high specific capacity, enhanced safety, and cost-effectiveness. However, the application of aqueous electrolytes introduces challenges: Zn dendrite formation and parasitic reactions at the anode, as well as dissolution, electrostatic interaction, and by-product formation at the cathode. In addressing these electrode-centric problems, additive engineering has emerged as an effective strategy. This review delves into the latest advancements in electrolyte additives for ZIBs, emphasizing their role in resolving the existing issues. Key focus areas include improving morphology and reducing side reactions during battery cycling using synergistic effects of modulating anode interface regulation, zinc facet control, and restructuring of hydrogen bonds and solvation sheaths. Special attention is given to the efficacy of amino acids and zwitterions due to their multifunction to improve the cycling performance of batteries concerning cycle stability and lifespan. Additionally, the recent additive advancements are studied for low-temperature and extreme weather applications meticulously. This review concludes with a holistic look at the future of additive engineering, underscoring its critical role in advancing ZIB performance amidst the complexities and challenges of electrolyte additives.

Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.

PURPOSE: To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. METHODS: NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via nonlinear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k-space-based SSDU loss on the region of interest. NLINV-Net-based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. RESULTS: NLINV-Net-based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. CONCLUSION: NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. METHODS: SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. RESULTS: We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10 × $$ 10\times $$ ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. CONCLUSION: Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

Management of phenylketonuria in European PKU centres remains heterogeneous.

Phenylketonuria (PKU) is a genetic disorder that follows an autosomal recessive inheritance pattern. Dietary treatment is the cornerstone of therapy and is based on natural protein restriction, Phe-free L-amino acid supplements (protein substitutes) and low protein foods. The aim of this project was to collect information about the clinical management of patients with PKU, focusing on understudied or unresolved issues such as blood phenylalanine (Phe) fluctuations and clinical symptoms, particularly gastro intestinal (GI) discomfort and sleep problems. The survey consisted of 10 open-ended and 12 multiple-choice questions that collected information about size of the PKU population in each center, the center's clinical practices and the outcomes observed by the center concerning adherence, clinical and biochemical abnormalities and clinical symptoms (GI and sleep). The questionnaire was sent to 72 experts from metabolic centers in 11 European countries. Thirty-three centers answered. The results of this survey provide information about the clinical practice in different age groups, concentrating on dietary tolerance, treatment adherence, and metabolic control. All the centers prescribed a Phe-restricted diet, with Phe-free/low Phe protein substitutes and low protein foods. Daily doses given of protein substitutes varied from 1 to 5, with adherence to the prescribed amounts decreasing with increasing age. Respondents identified that improvement in the flavor, taste, volume and smell of protein substitutes may improve adherence. Finally, the survey showed that clinical symptoms, such as GI discomfort and sleep problems occur in patients with PKU but are not systematically evaluated. Twenty-four-hour Phe fluctuations were not routinely assessed. The results highlight a strong heterogeneity of approach to management despite international PKU guidelines. More clinical attention should be given to gastrointestinal and sleep problems in PKU.

A comparison of procedure-related adverse events between two right ventricular leadless pacemakers.

INTRODUCTION: The Medtronic Micra VR and Abbott AVEIR VR are the leadless pacemakers (LPM) currently available in the United States (US). Micra VR employs fixation tines and the AVEIR VR uses an active fixation helix. Micra VR requires fixation before electrical measurements are obtained, while R-waves may be mapped by AVEIR VR without fixation. Little comparative data is available for these LPMs. Accordingly, we compared the incidences of procedure-related major adverse clinical events (MACE) and device problems in the US for Micra VR and AVEIR VR during 2022-2024. METHODS: We searched the FDA's Manufacturer and User Facility Device Experience (MAUDE) database for US reports of MACE and device problems that were filed from April 2022 to December 2023 for AVEIR VR, and from June 2022 to April 2024 for Micra VR. Totals for US-registered LPM implants were obtained from the manufacturers' product performance reports. RESULTS: During the study period, 5990 AVEIR VR and 10 940 Micra VR implants were registered in the US. We found 305 MAUDE reports for AVEIR VR (5.1%), versus 541 MAUDE reports for Micra VR (4.9%) (p = .702). The incidence of MACE was 0.72% (43/5990) for AVEIR VR versus 0.59% (65/10 940) for Micra VR, (p = .387). The incidences of procedure-related death, cardiac perforation. cardiac arrest, emergency pericardial drainage or reparative surgery were similar for both LPMs (p > .05). Micra VR had more unacceptable thresholds requiring LPM replacement compared to AVEIR VR (95;0.9% vs. 24;0.4%; p = .001). AVEIR VR had a statistically higher incidence of device dislodgement during (32) and after (21) implant compared to Micra VR (53 (0.9%) vs. 46 (0.4%), p < .001). CONCLUSIONS: Micra VR and AVEIR VR have similar procedural safety profiles, including the incidences of death and perforation. However, device problems differed significantly, possibly related to their design differences. Compared to Micra VR, AVEIR VR appears to have an advantageous threshold measurement capability but is more prone to device dislodgement.

Clinical characteristics, longitudinal adaptive functioning, and association with electroencephalogram activity in PPP2R5D-related neurodevelopmental disorder.

Protein phosphatase 2 regulatory subunit B56δ related neurodevelopmental disorder (PPP2R5D-related NDD) is largely caused by de novo heterozygous missense PPP2R5D variants. We report medical characteristics, longitudinal adaptive functioning, and in-person neurological, motor, cognitive, and electroencephalogram (EEG) activity for PPP2R5D-related NDD. Forty-two individuals (median age 6 years, range = 0.8-25.3) with pathogenic/likely pathogenic PPP2R5D variants were assessed, and almost all variants were missense (97.6%) and de novo (85.7%). Common clinical symptoms were developmental delay, hypotonia, macrocephaly, seizures, autism, behavioral challenges, and sleep problems. The mean Gross motor functional measure-66 was 60.2 ± 17.3% and the mean Revised upper limb module score was 25.9 ± 8.8. The Vineland-3 adaptive behavior composite score (VABS-3 ABC) at baseline was low (M = 61.7 ± 16.8). VABS-3 growth scale value scores increased from baseline in all subdomains (range = 0.6-5.9) after a mean follow-up of 1.3 ± 0.3 years. EEG beta and gamma power were negatively correlated with VABS-3 score; p < 0.05. Individuals had a mean Quality-of-life inventory-disability score of 74.7 ± 11.4. Twenty caregivers (80%) had a risk of burnout based on the Caregiver burden inventory. Overall, the most common clinical manifestations of PPP2R5D-related NDD were impaired cognitive, adaptive function, and motor skills; and EEG activity was associated with adaptive functioning. This clinical characterization describes the natural history in preparation for clinical trials.

Duality and the well-posedness of a martingale problem.

For two Polish state spaces EX and EY, and an operator GX, we obtain existence and uniqueness of a GX-martingale problem provided there is a bounded continuous duality function H on EX×EY together with a dual process Y on EY which is the unique solution of a GY-martingale problem. For the corresponding solutions [Formula: see text] and [Formula: see text] , duality with respect to a function H in its simplest form means that the relation Ex[H(Xt,y)]=Ey[H(x,Yt)] holds for all (x,y)∈EX×EY and t≥0. While duality is well-known to imply uniqueness of the GX-martingale problem, we give here a set of conditions under which duality also implies existence without using approximating sequences of processes of a different kind (e.g. jump processes to approximate diffusions) which is a widespread strategy for proving existence of solutions of martingale problems. Given the process [Formula: see text] and a duality function H, to prove existence of [Formula: see text] one has to show that the r.h.s. of the duality relation defines for each y a measure on EX, i.e. there are transition kernels [Formula: see text] from EX to EX such that Ey[H(x,Yt)]=∫µt(x,dx')H(x',y) for all (x,y)∈EX×EY and all t≥0. As examples, we treat resampling and branching models, such as the Fleming-Viot measure-valued diffusion and its spatial counterparts (with both, discrete and continuum space), as well as branching systems, such as Feller's branching diffusion. While our main result as well as all examples come with (locally) compact state spaces, we discuss the strategy to lift our results to genealogy-valued processes or historical processes, leading to non-compact (discrete and continuum) state spaces. Such applications will be tackled in forthcoming work based on the present article.

The relationship between weight-related indicators and depressive symptoms during adolescence and adulthood: results from two twin studies.

BACKGROUND: The association between weight and depressive symptoms is well established, but the direction of effects remains unclear. Most studies rely on body mass index (BMI) as the sole weight indicator, with few examining the aetiology of the association between weight indicators and depressive symptoms. METHODS: We analysed data from the Twins Early Development Study (TEDS) and UK Adult Twin Registry (TwinsUK) (7658 and 2775 twin pairs, respectively). A phenotypic cross-lagged panel model assessed the directionality between BMI and depressive symptoms at ages 12, 16, and 21 years in TEDS. Bivariate correlations tested the phenotypic association between a range of weight indicators and depressive symptoms in TwinsUK. In both samples, structural equation modelling of twin data investigated genetic and environmental influences between weight indicators and depression. Sensitivity analyses included two-wave phenotypic cross-lagged panel models and the exclusion of those with a BMI <18.5. RESULTS: Within TEDS, the relationship between BMI and depression was bidirectional between ages 12 and 16 with a stronger influence of earlier BMI on later depression. The associations were unidirectional thereafter with depression at 16 influencing BMI at 21. Small genetic correlations were found between BMI and depression at ages 16 and 21, but not at 12. Within TwinsUK, depression was weakly correlated with weight indicators; therefore, it was not possible to generate precise estimates of genetic or environmental correlations. CONCLUSIONS: The directionality of the relationship between BMI and depression appears to be developmentally sensitive. Further research with larger genetically informative samples is needed to estimate the aetiological influence on these associations.