Multidimensional Patterns Research Articles

Multidimensional patterns of bird diversity and its driving forces in the Yangtze river basin of China

Biodiversity is fundamental to human well-being and economic development. The Yangtze River, the largest river in China, faces biodiversity loss due to habitat degradation, climate change, and other anthropogenic threats. However, the long-term changes in the region's biodiversity remain poorly understood. Here, we constructed an optimized living planet index (LPIO) by combining Partial Least Squares Structural Equation Modeling and Random Forest Modeling. Using data from a monitoring network of 536 sites, we observed an increasing trend in terrestrial bird diversity and functional complexity across the entire watershed from 2011 to 2020. Our findings indicate that a large-scale ecological restoration program has contributed to increases in terrestrial and aquatic bird diversity in the Yangtze River Basin. In contrast, bird diversity in the downstream area has decreased by 2.83 ​%, largely due to a rapid decline in wetland birds. The degradation of wetland habitats and insufficient conservation measures have negatively impacted bird diversity in the downstream region. This suggests that although there have been significant improvements in terrestrial bird diversity, more effective wetland restoration is necessary for biodiversity conservation. We recommend optimizing the national large-scale biodiversity monitoring network and increasing the number of upstream monitoring sites.

Chinese Studies: Researches and services in the National Library of China

Background The Chinese Studies Center of the National Library of China is an institution focusing on the collection, research and service of Chinese studies literature. Since the last century, the National Library of China (NLC) has started to collect a wide range of Chinese studies literature, and has now formed a huge special collection covering multiple languages and disciplines. Purpose This article introduces the origin of construction of the Chinese Studies Center (CSC), its service positioning, development status, major achievements and future outlook, with the aim of clarifying ideas for better development of the NLC’s Chinese studies collection and research services, and for the promotion of the development of Chinese studies to a higher level. Research Design The article aims to build a research framework to assess the role and potential of the CSC in promoting Chinese studies by reviewing its history and analyzing its service orientation and development direction. Study Sample: The research sample includes the CSC's resources, services, accomplishments and cooperations. Data Collection and/or Analysis: The article assesses the performance and influence of the CSC by collecting and analyzing information on its resources, services, achievements and academic exchanges. Results The CSC has basically formed a multi-dimensional development pattern integrating reading services, consulting and reference services, literature research, website construction, scientific research cooperation and foreign exchanges. Its service positioning has become clearer, its responsibilities richer, its research capability further enhanced, and it has achieved social benefits and a series of good results, making a great contribution to the development of Chinese studies. Conclusions The CSC has played an important in promoting Chinese studies and it will continue to enrich the collection of Chinese studies literature, promote the construction of Chinese studies catalogue, create a one-stop research and service platform, and carry out exchanges and cooperation in Chinese studies worldwide, so as to promote the further development of Chinese studies.

Impairment of Cardiovascular Functional Capacity in Mild-to-Moderate Kidney Dysfunction.

Traditional diagnostic tools that assess resting cardiac function and structure fail to accurately reflect cardiovascular alterations in patients with chronic kidney disease (CKD). This study sought to determine whether multidimensional exercise response patterns related to cardiovascular functional capacity can detect abnormalities in mild-to-moderate CKD. In a cross-sectional study, we examined 3,075 participants from the Framingham Heart Study (FHS) and 451 participants from the Massachusetts General Hospital Exercise Study (MGH-ExS) who underwent cardiopulmonary exercise testing (CPET). Participants were stratified by estimated glomerular filtration rate (eGFR): eGFR ≥90; eGFR 60-89; eGFR 30-59. Our primary outcomes of interest were peak oxygen uptake (VO 2 Peak),VO 2 at anaerobic threshold (VO 2 AT), and the ratio of minute ventilation to carbon dioxide production (VE/VCO 2 ). Multiple linear regression models were fitted to evaluate the associations between eGFR group and each outcome variable adjusted for covariates. In the FHS cohort, N=1,712 (56%) had an eGFR ≥90 ml/min/1.73m 2 , N=1,271 (41%) had an eGFR 60-89 ml/min/1.73m 2 , and N=92 (3%) had an eGFR 30-59 ml/min/1.73m 2 . In the MGH-ExS cohort, N=247 (55%) had an eGFR ≥90 ml/min/1.73m 2 , N=154 (34%) had an eGFR 60-89 ml/min/1.73m 2 , and N=50 (11%) had an eGFR 30-59 ml/min/1.73m 2 . In FHS, VO 2 Peak and VO 2 AT were incrementally impaired with declining kidney function ( p <0.001); however this pattern was attenuated following adjustment for age. Percent-predicted VO 2 Peak at AT was higher in the lower eGFR groups ( p <0.001). In MGH-ExS, VO 2 Peak and VO 2 AT were incrementally impaired with declining kidney function in unadjusted and adjusted models ( p <0.05). VO 2 Peak was associated with eGFR ( p <0.05) in all models even after adjusting for age. On further mechanistic analysis, we directly measured cardiac output (CO) at peak exercise via right heart catheterization and found impaired CO in the lower eGFR groups ( p ≤0.007). CPET-derived indices may detect impairment in cardiovascular functional capacity and track cardiac output declines in mild to moderate CKD.

Genetic determinants of antidepressant and antipsychotic drug response.

Today, more than 90% of inpatients hospitalized with Major Depression or Schizophrenia are treated with psychotropic drugs. Since none of the treatment options is causal, response rates are modest and the course of recovery is very heterogeneous. Genetic studies on the etiology and pathogenesis of major psychiatric disorders over the past decades have been largely unsuccessful. Likewise, genetic studies to predict response to psychopharmacological treatment have also not been particularly successful. In this project we have recruited 902 inpatients with ICD-10 diagnoses of schizophrenic ("F2 patients") or depressive disorders ("F3 patients"). The study assessed today's acute inpatient treatment regimens with up to 8 repeated measurements regarding the time course of recovery and adverse side effects. The genotyping included 100 candidate genes with genotypic patterns computed from 549 Single Nucleotide Polymorphisms (SNPs). To predict response to psychopharmacological treatment, we relied on a multidimensional approach to analyzing genetic diversity in combination with multilayer Neural Nets (NNs). Central to this new method were the "gene vectors" that (1) assessed the multidimensional genotypic patterns observed with genes; and (2) evaluated the correlations between genes. By means of these methods, we searched for combinations of multidimensional genotypic patterns that were characteristic of treatment responders while being rare among non-responders. The chosen method of approach provided a powerful technique to detail the complex structures of SNP data that are not detectable by conventional association methods. Molecular-genetic NNs enabled correct classification of 100% "non-responders", along with 94.7% correctly classified "responders" among the F2 patients, and 82.6% correctly classified "responders" among the F3 patients. The F2 and F3 classifiers were not disjoint but showed an overlap of 29.6% and 35.7% between the diagnostic groups, thus indicating that clinical diagnoses may not constitute etiologic entities. Our results suggested that patients may have an unspecific physical-genetic disposition that enables, facilitates, impedes or prevents recovery from major psychiatric disorders by setting various thresholds for exogenous triggers that initiate improvement ("recovery disposition"). Even though this disposition is not causally linked to recovery, it can nonetheless be clinically used in the sense of a "surrogate". Indeed, clinicians are also interested in reliable tools that can "do the job", despite the fact that etiology and pathogenesis of the treated disorders remain unknown.

Psychometric Evaluation of Cognitive and Positive Valence Tasks Chosen for the NIMH Research Domain Criteria Project.

The NIMH Research Domain Criteria (RDoC) initiative seeks to utilize multidimensional patterns of socio-cognitive behavior to improve understanding of mental illness. We aimed to evaluate the psychometric properties of a subset of RDoC tasks. Specifically, we investigated two positive valence tasks and five cognitive tasks. Participants (N = 320) were recruited through an online research platform. We used generalizability theory to estimate reliability, and factor analysis to examine factor structure. Reliability was average to excellent with some notable exceptions. Factor analysis results raised concerns about whether the factor structure of task scores aligns with the proposed RDoC model. Effects of cognitive manipulations generally supported the construct representation of tasks. Results indicate that the majority of RDoC task scores examined have acceptable reliability or can be made reliable through modest increases in task length. Future research in diverse populations is needed to better understand the factor structure of RDoC cognitive and positive valence measures.

Multidimensional patterns of early care and education access through a family centered lens

Despite evidence of the benefits of early care and education (ECE) for child development and family employment, the supply of providers is scarce and variable; leading many families with young children to experience limited and inequitable access. To examine the multidetermined nature of access, this study leverages a multidimensional, family-centered definition and a nationally representative sample of families of preschoolers from the Early Childhood Longitudinal Study – Birth Cohort to examine (1) patterns of access-related features present in preschool-age children's ECE arrangements, and (2) child characteristics, household circumstances, and care setting type that may be related to patterns of access. Latent class analysis model enumeration revealed five latent classes of children's nonparental care arrangements along features of accessibility: High Across Most Access Features (26.4 %), Single, Long-Term Provider (30.1 %), Limited Across Most Access Features (11.6 %), Low Affordability, Multiple Providers (10.1 %), and High Affordability, Recent Transition (21.8 %). These results suggested many children were in ECE that met multiple dimensions of access, with others in care arrangements that reflected trade-offs. Children were also differentially classified into types of ECE arrangements in relation to care setting type, race/ethnicity, income, household urbanicity, and parental employment. The implications of investigating ECE access as a multidimensional construct, and recommendations for how ECE providers and policy can more closely align with family needs are discussed.

Neurophysiological Basis of Electroacupuncture Stimulation in the Treatment of Cardiovascular-Related Diseases: Vagal Interoceptive Loops.

The vagal sensory nerve (VSN) is an essential interoceptive pathway that is connected to every level of the body. Its intricate genetic coding provides sustenance for physiological processes, including controlling blood pressure and respiration. Electroacupuncture (EA) is a proven surface stimulation therapy that can regulate vagal nerve activity, which can effectively prevent cardiovascular diseases. A growing number of studies have concentrated on the mapping of VSN codes, but little is known, and the physiological background of how EA influences interoceptive has not been fully explored. Here, we incorporate the hypothesized interaction among EA targets, VSNs, and the heart. This offers suggestions for using a versatile and focused EA strategy to modify vagal interoceptive awareness to enhance cardiovascular conditions. We first clarified the major role of vagal nerve in the control of cardiac activity. Additionally, we clarified the multidimensional coding pattern in the VSNs, revealing that the targeted control of multimodal interoceptive is the functional basis of the synchronization of cardiovascular system. We propose a strategy in which EA of the VSNs is employed to activate the interoceptive loop and reduce the risk of cardiovascular disease.

Strong conservatism in leaf anatomical traits and their multidimensional relationships with leaf economic traits in grasslands under different stressful environments

BackgroundPlant traits and plant adaptive strategies have been affected by the increasing intensity and severity of environmental changes. Given the uncertainty surrounding future environmental conditions, investigating plant trait variations under various stresses is crucial for unraveling plant survival strategies. Leaf anatomical traits are closely responsible for plants’ photosynthesis, respiration and transpiration. However, knowledge of how the multi-species leaf anatomical traits varied in extremely and moderately stressful environments is limited. Our objective was to compare the variation of leaf anatomic traits and adaptation strategies in two different stressful regions of the Qinghai-Tibet Plateau (TP) and Mongolian Plateau (MP) of China.MethodsWe sampled ten sites in each of the two regions (MP and TP) along an environmental gradient. Seven leaf anatomical traits and two leaf economic traits were measured for all leaf samples. Leaf anatomical traits include the traits related to leaf physiological processes (mesophyll thickness (MT), palisade tissue thickness (PT), spongy tissue thickness (ST), palisade-spongy tissue thickness ratio (PST) and epidermal thickness (ET)) and the traits related to trait construction investment (epiderm-leaf thickness ratio (ET/LT) and mesophyll-leaf thickness ratio (MT/LT)). Leaf economic traits include specific leaf area (SLA) and leaf nitrogen content (LN).ResultsThe results revealed that leaf anatomical traits in the TP exhibited greater phylogenetic conservation with thicker structures, being less susceptible to environmental impacts than those in the MP. Additionally, the leaf anatomical and economic traits decoupled both in the MP and TP.ConclusionThese findings highlight that plants adopt diverse strategies to cope with extremely and moderately environmental stresses, but multidimensional trait patterns are generally favored in stressful environments.

Modelling the maximum ceiling temperature with bifurcated plume in tunnel fires

Evaluation of the temperature profile induced by tunnel fire is of great significance for fire detection and structural damage assessment. In this work, a special plume model, namely, the “plume bifurcation” is introduced, and its impact on the temperature distribution is analysed theoretically. Full-scale tunnel fire experiments and numerical simulations are conducted to investigate the plume evolution under various heat release rates and ventilation conditions. Results show that the plume will sperate into two sub-streams under strong ventilation condition, resulting in the multi-dimensional plume movement pattern when rising. The sub-plume impingement point location, quantified by the longitudinal vertical deflection angle θv and horizontal bifurcation angle θh, is correlated with the fire heat release rate and ventilation condition. The plume bifurcation angle is 0 when the dimensionless ventilation velocity V′≤0.33, marking a single-stream plume; and rapidly increase and then slowly decrease with V′ when V′>0.33, representing the sudden occurrence and decay trend of plume bifurcation with the ventilation velocity. Finally, a novel bifurcated plume-based model to estimate the maximum ceiling temperature is proposed. This study reveals the role of multi-dimensional smoke movement in reshaping the temperature field, providing new perspectives for a deeper understanding of the smoke transport behaviour in longitudinally ventilated tunnel fires.

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques. Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features. Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data. The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI. This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.

Laser Fabrication of Multi-Dimensional Perovskite Patterns with Intelligent Anti-Counterfeiting Applications.

Perovskites have gained widespread attention across various fields such as photovoltaics, displays, and imaging. Despite their promising applications, achieving precise and high-quality patterning of perovskite films remains a challenge. In this study, femtosecond laser direct writing technology is utilized to achieve rapid and highly precise micro/nanofabrication on perovskites. The study successfully fabricates multiple structured and emission-tunable perovskite patterns composed of A2(FA)n-1PbnX3n+1 (A represents a series of long-chain amine cations, and X = Cl, Br, I), encompassing 2D, quasi-2D, and 3D structures. The study delves into the intricate interplay between fabrication technology and the growth of multi-dimensional perovskites: higher repetition rates, coupled with appropriate laser power, prove more conducive to perovskite growth. By employing precise halogen element design, the simultaneous generation of two distinct color quick-response (QR) code patterns is achieved through one-step laser processing. These mirrored QR codes offer a novel approach to anti-counterfeiting. To further enhance anti-counterfeiting capabilities, artificial intelligence (AI)-based methods are introduced for recognizing patterned perovskite anti-counterfeiting labels. The combination of deep learning algorithms and a non-deterministic manufacturing process provides a convenient means of identification and creates unclonable features. This integration of materials science, laser fabrication, and AI offers innovative solutions for the future of security features.

Clusters of multidimensional exercise response patterns and estimated heart failure risk in the Framingham Heart Study.

New tools are needed to identify heart failure (HF) risk earlier in its course. We evaluated the association of multidimensional cardiopulmonary exercise testing (CPET) phenotypes with subclinical risk markers and predicted long-term HF risk in a large community-based cohort. We studied 2532 Framingham Heart Study participants [age 53±9years, 52% women, body mass index (BMI) 28.0±5.3kg/m2, peak oxygen uptake (VO2) 21.1±5.9kg/m2 in women, 26.4±6.7kg/m2 in men] who underwent maximum effort CPET and were not taking atrioventricular nodal blocking agents. Higher peak VO2 was associated with a lower estimated HF risk score (Spearman correlation r: -0.60 in men and -0.55 in women, P<0.0001), with an observed overlap of estimated risk across peak VO2 categories. Hierarchical clustering of 26 separate CPET phenotypes (values residualized on age, sex, and BMI to provide uniformity across these variables) identified three clusters with distinct exercise physiologies: Cluster 1-impaired oxygen kinetics; Cluster 2-impaired vascular; and Cluster 3-favourable exercise response. These clusters were similar in age, sex distribution, and BMI but displayed distinct associations with relevant subclinical phenotypes [Cluster 1-higher subcutaneous and visceral fat and lower pulmonary function; Cluster 2-higher carotid-femoral pulse wave velocity (CFPWV); and Cluster 3-lower CFPWV, C-reactive protein, fat volumes, and higher lung function; all false discovery rate<5%]. Cluster membership provided incremental variance explained (adjusted R2 increment of 0.10 in women and men, P<0.0001 for both) when compared with peak VO2 alone in association with predicted HF risk. Integrated CPET response patterns identify physiologically relevant profiles with distinct associations to subclinical phenotypes that are largely independent of standard risk factor-based assessment, which may suggest alternate pathways for prevention.

Social demographics imputation based on similarity in multi-dimensional activity-travel pattern: A two-step approach

In response to the absence of demographics in increasingly emerging big data sets, we propose a novel method for inferring the missing demographic information based on similarity in people’s daily multi-dimensional activity-travel patterns as well as the characteristics of the area they move about. Instead of using isolated activity-travel attributes to infer social demographic features, our proposed method first calculates the similarity of people’s multidimensional daily activities and travels as well as characteristics of their visiting locations, between those for whom the social demographics are to be imputed (target) and those with known demographics (base) using a polynomial function. The weights of the function are determined using the permutation feature importance method, and then dynamic time warping is used to align the multidimensional activity sequences of the base and target sample and measure their similarities. For each person in the target database, a matched list is created consisting of those with the most similar activity-travel sequences in the base sample. A support vector machine is then trained using the base sample as input to impute the demographics of the target sample. The proposed model is trained using a national travel survey and validated by applying it to a GPS dataset. The results show that the proposed method outperforms existing methods in predicting four selected demographics: gender, age, education level, and work status, with an accuracy range between 91% and 94% for the national dataset and 88% to 91% for the GPS data. This study highlights the importance of considering the multidimensional and sequential nature of peoples’ daily activity-travel patterns in the imputation of demographic features.

Curve-Modelling and Machine Learning for a Better COPD Diagnosis.

Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

Multidimensional polynomial patterns over finite fields: Bounds, counting estimates and Gowers norm control

We examine multidimensional polynomial progressions involving linearly independent polynomials over finite fields, proving power saving bounds for sets lacking such configurations. This jointly generalises earlier results of Peluse (for the single dimensional case) and the author (for distinct degree polynomials). In contrast to the cases studied in the aforementioned two papers, a usual PET induction argument does not give Gowers norm control over multidimensional progressions that involve polynomials of the same degrees. The main challenge is therefore to obtain Gowers norm control, and we accomplish this for all multidimensional polynomial progressions with pairwise independent polynomials. The key inputs are: (1) a quantitative version of a PET induction scheme developed in ergodic theory by Donoso, Koutsogiannis, Ferré-Moragues and Sun, (2) a quantitative concatenation result for Gowers box norms in arbitrary finite abelian groups, motivated by (but different from) earlier results of Tao, Ziegler, Peluse and Prendiville; (3) an adaptation to combinatorics of the box norm smoothing technique, recently developed in the ergodic setting by the author and Frantzikinakis; and (4) a new version of the multidimensional degree lowering argument.

26. Understanding Cognitive Development by Capturing Complex, Multidimensional, Childhood Environments and Individual-Specific Patterns of Functional Brain Network Organization

26. Understanding Cognitive Development by Capturing Complex, Multidimensional, Childhood Environments and Individual-Specific Patterns of Functional Brain Network Organization

Detritivores maintain stoichiometric homeostasis, but alter body size and population density in response to altitude induced stoichiometric mismatches

Maintaining a balance between growth needs and available food resources is critical to the development of any organism. Ecological stoichiometry provides a theoretical basis for studying stoichiometric mismatches between organisms and their food resources. Recent studies have shown that detritivore taxa occupying different multidimensional stoichiometric niches (MSNs) exhibit different stoichiometric mismatch patterns. However, changes in stoichiometric mismatch patterns of different functional groups within the same taxon in response to changes in food resources are poorly understood. Here, we investigated changes in the stoichiometric mismatch between litter as major food resource and five functional groups of soil detritivores (millipedes; Diplopoda) for eleven elements along an altitudinal gradient from 800 to 1850 m on Changbai Mountain, Northeast China. We quantified MSNs and multidimensional stoichiometric mismatch patterns, and investigated changes with altitude and relationships with body size and population density. The concentration of chemical elements of litter varied significantly with altitude and this also was true for the trophic stoichiometric mismatch in millipedes. Furthermore, millipede stoichiometry homeostasis resulted in changing stoichiometric mismatches with changing litter element ratios. Element concentrations in litter, specifically N, Ca, P, Na and Cu, impacted the body size and population density of millipedes, with different elements correlating with individual functional groups. The results indicate that consumer-resource stoichiometric mismatches significantly affect detritivore populations, highlighting the predictive power of the multidimensional stoichiometry framework for detritivore community composition in context of environmental change.

Empirically derived profiles of neurocognitive functioning in youth and young adults with sickle cell disease.

Sickle cell disease (SCD) is an inherited blood disorder associated with neurocognitive deficits. In contrast to variable-centered approaches, no known research has utilized person-centered strategies to identify multidimensional patterns of neurocognitive functioning of an individual with SCD. The purpose of the present study was to create empirically derived profiles and identify predictors of neurocognitive functioning subgroups among youth and young adults with SCD. Individuals with SCD (N = 393, mean age 14.05 years, age range 8-24, 50.4% female/49.6% male) completed neurocognitive assessments. Latent profile analysis derived subgroups/classes of neurocognitive functioning and determined relations with demographic and medical variables. Three latent classes emerged: average functioning (n = 102, 27%), low average functioning (n = 225, 60%), and exceptionally low functioning (n = 46, 12%). Older age was associated with membership in the low average and exceptionally low functioning groups (relative to the average group). Being prescribed hydroxyurea was associated with membership in the average functioning group (relative to the low average group) and absence of hydroxyurea use was associated with membership in the exceptionally low group (relative to the low average group). Lower social vulnerability was associated with membership in the average functioning group compared to the low average and exceptionally low groups. Clinicians can help reduce disparities in cognitive development for individuals with SCD by promoting early treatment with hydroxyurea and implementing methods to reduce social vulnerabilities that can interfere with access to evidence-based care.

Persistent Food Insecurity and Material Hardships: A Latent Class Analysis of Experiences among Venezuelan Refugees and Migrants in Urban Colombia.

The causes and conditions of displacement often increase the vulnerability of migrant and refugee populations to food insecurity, alongside other material hardships. We aimed to examine the multidimensional aspects and patterns of food insecurity and other material hardships in a cross-sectional sample of 6221 Venezuelan refugees and migrants in urban Colombia using a latent class analysis. Using multinomial and logistic regression models, we investigated the demographic and migratory experiences associated with identified classes and how class membership is associated with multiple health outcomes among Venezuelan refugees and migrants, respectively. Approximately two thirds of the sample was comprised cisgender women, and the participants had a median age of 32 years (IQR: 26-41). Four heterogeneous classes of food insecurity and material hardships emerged: Class 1-low food insecurity and material hardship; Class 2-high food insecurity and material hardship; Class 3-high income hardship with insufficient food intake; and Class 4-income hardship with food affordability challenges. Class 2 reflected the most severe food insecurity and material hardships and had the highest class membership; Venezuelans with an irregular migration status were almost 1.5 times more likely to belong to this class. Food insecurity and material hardship class membership was independently associated with self-rated health, mental health symptoms, and recent violence victimization and marginally associated with infectious disease outcomes (laboratory-confirmed HIV and/or syphilis infection). Social safety nets, social protection, and other interventions that reduce and prevent material hardships and food insecurity among refugees and migrants, alongside the host community, may improve public health, support development, and reduce healthcare costs. In the long term, regularization and social policies for migrants aimed at enhancing refugees' and migrants' social and economic inclusion may contribute to improving food security in this population.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms—including the number of genes necessary for the formation of the target spatial pattern—we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.