Maternal glucose homeostasis during pregnancy in women with overweight or obesity and offspring metabolic health.

Gestational diabetes mellitus (GDM) adversely affects offspring glucose homeostasis and risk of developing obesity. Here, we examined the association between glycemia in pregnant women with overweight or obesity without GDM and offspring metabolic health. Maternal fasting glucose concentrations and glucose 2-h after an oral glucose tolerance test (OGTT) were measured in 208 women with a pre-pregnancy body mass index (BMI) of 28-45 kg/m2 without GDM. Offspring outcomes were collected at birth, 3, and 5 years of age. Linear mixed models with time as fixed factor and subject ID as random effects were used for analysis. No associations were found between maternal fasting or 2-h glucose concentrations with offspring glucose and insulin concentrations from birth to 5 years of age. However, maternal fasting glucose in GW 28 and 36, and 2-h OGTT glucose in GW 28 were positively associated with C-peptide concentration at birth. Maternal fasting glucose concentrations in GW 28 and 36 were positively associated with weight-for-length, and maternal fasting glucose in GW 36 was associated with BMI z-score at birth. In summary, blood glucose in pregnant women with overweight or obesity is positively associated with offspring C-peptide concentration, weight-for-length, and BMI z-score at birth, even in the absence of GDM.

Hispanic Thrifty Food Plan (H-TFP): Healthy, Affordable, and Culturally Relevant.

The USDA Thrifty Food Plan (TFP) is a federal estimate of a healthy diet at lowest cost for US population groups defined by gender and age. The present goal was to develop a version of the TFP that was more tailored to the observed dietary patterns of self-identified Hispanic participants in NHANES 2013-16. Analyses used the same national food prices and nutrient composition data as the TFP 2021. Diet quality was measured using the Healthy Eating Index 2015. The new Hispanic TFP (H-TFP) was cost-neutral with respect to TFP 2021 and fixed at $186/week for a family of four. Two H-TFP models were created using a quadratic programming (QP) algorithm. Fresh pork was modeled separately from other red meats. Hispanic NHANES participants were younger, had lower education and incomes, but had similar or higher HEI 2015 scores than non-Hispanics. Their diet included more pulses, beans, fruit, 100% juice, grain-based dishes, and soups, but less pizza, coffee, candy, and desserts. The H-TFP market basket featured more pork, whole grains, 100% fruit juice, and cheese. The second TFP model showed that pork could replace both poultry and red meat, while satisfying all nutrient needs. A vegetarian H-TFP proved infeasible for most age-gender groups. Healthy, affordable, and culturally relevant food plans can be developed for US population subgroups.

Rapid Growth between 0 and 2 Years Old in Healthy Infants Born at Term and Its Relationship with Later Obesity: A Systematic Review and Meta-Analysis of Evidence.

INTRODUCTION: Rapid growth in early childhood has been identified as a possible risk factor for long-term adiposity. However, there is a lack of studies quantifying this phenomenon only in healthy, full-term infants with appropriate birth weight for gestational age. This systematic review and meta-analysis aimed to investigate the association of rapid growth in full-term children up to 2 years of age with adiposity up to 18 years of age. METHODOLOGY: A systematic review of the literature was conducted in PubMed, EMBASE, and Web of Science. RESULTS: 14 studies were included. We were unable to find strong evidence that rapid growth in early childhood is a risk factor for long-term adiposity. Rapid growth in early childhood was associated with taller heights (standardized mean difference: 0.51 (CI: 0.25-0.77)) and higher body mass index (standardized mean difference: 0.50 (CI: 0.25-0.76)) and a higher risk of overweight under 18 years. CONCLUSION: Rapid growth in early childhood in term infants with appropriate birth weight is associated with higher growth, body mass index, and risk of being overweight up to age 18, but further work is needed to identify the associations between early rapid growth and obesity later in adulthood.

Adaptive Finite-Time-Based Neural Optimal Control of Time-Delayed Wheeled Mobile Robotics Systems.

For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton-Jacobi-Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed.

Research on Trajectory Planning of Autonomous Vehicles in Constrained Spaces.

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments.

Point Cloud Densification Algorithm for Multiple Cameras and Lidars Data Fusion.

Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.

Influence of Infill Patterns on the Shape Memory Effect of Cold-Programmed Additively Manufactured PLA.

In four-dimensional additive manufacturing (4DAM), specific external stimuli are applied in conjunction with additive manufacturing technologies. This combination allows the development of tailored stimuli-responsive properties in various materials, structures, or components. For shape-changing functionalities, the programming step plays a crucial role in recovery after exposure to a stimulus. Furthermore, precise tuning of the 4DAM process parameters is essential to achieve shape-change specifications. Within this context, this study investigated how the structural arrangement of infill patterns (criss-cross and concentric) affects the shape memory effect (SME) of compression cold-programmed PLA under a thermal stimulus. The stress-strain curves reveal a higher yield stress for the criss-cross infill pattern. Interestingly, the shape recovery ratio shows a similar trend across both patterns at different displacements with shallower slopes compared to a higher shape fixity ratio. This suggests that the infill pattern primarily affects the mechanical strength (yield stress) and not the recovery. Finally, the recovery force increases proportionally with displacement. These findings suggest a consistent SME under the explored interval (15-45% compression) despite the infill pattern; however, the variations in the mechanical properties shown by the stress-strain curves appear more pronounced, particularly the yield stress.

Neighborhood environments underpin screen time intervention success in children: Evidence from a study of greenspace and community programming across 130 US communities.

The goal of this study was to understand how neighborhood greenspace access may support or hinder the effectiveness of community programs and policies (CPPs) aimed at reducing racial and ethnic inequities in screen time among 4598 US children. We found higher CPP intensity was significantly associated with fewer screen time behaviors in high greenspace neighborhoods, but not neighborhoods with low or moderate greenspace. Moreover, there were significant differences in greenspace access by neighborhood-level race and ethnicity. Implementing CPPs without regard for racial and ethnic greenspace inequities may be an underlying cause in the perpetuation of inequities in childhood screen time.

A hybrid, nonlinear programming approach for optimizing passive shimming in MRI.

BACKGROUND: In magnetic resonance imaging (MRI), maintaining a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using linear programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. PURPOSE: In this work, we aimed to improve the efficacy of passive shimming that has the advantages of balancing field quality, iron usage, and harmonics in an optimal manner and leads to a smoother field profile. METHODS: This study introduces a hybrid algorithm that combines particle swarm optimization with sequential quadratic programming (PSO-SQP) to enhance shimming performance. Additionally, a regularization method is employed to reduce the iron pieces' weight effectively. RESULTS: The simulation study demonstrated that the magnetic field was improved from 462  to 3.6 ppm, utilizing merely 1.2 kg of iron in a 40 cm diameter spherical volume (DSV) of a 7T MRI magnet. Compared to traditional optimization techniques, this method notably enhanced magnetic field uniformity by 96.7% and reduced the iron weight requirement by 81.8%. CONCLUSION: The results indicated that the proposed method is expected to be effective for passive shimming.

Feeding parenteral nutrition in the neonatal period programs dyslipidemia in adulthood in Yucatan miniature pigs.

BACKGROUND: Early nutritional challenges can lead to permanent metabolic changes, increasing risk for developing chronic diseases later in life. Total parenteral nutrition (TPN) is a life-saving nutrition regimen, used especially in intrauterine growth-restricted (IUGR) neonates. Early TPN feeding alters metabolism, but whether these alterations are permanent is unclear. Programmed metabolism is likely caused by epigenetic changes due to imbalances of methyl nutrients. OBJECTIVES: We sought to determine whether feeding TPN in early life would increase the risk of developing dyslipidemia in adulthood and whether supplementing the methyl nutrients betaine and creatine to TPN would prevent this development. We also sought to determine whether IUGR exacerbates the effects of neonatal TPN on lipid metabolism in adulthood. METHODS: Female piglets (n=32, 7 d old) were used in four treatments: 24 normal weight piglets were randomized to sow-fed (SowFed), standard TPN (TPN-control), and TPN with betaine and creatine (TPN-B+C); 8 IUGR piglets were fed control TPN (TPN-IUGR) as a fourth group. After 2 weeks of treatment, all pigs were then fed a standard solid diet. At 8 mo old, central venous catheters were implanted to conduct postprandial fat tolerance tests. RESULTS: Feeding TPN in the neonatal period led to dyslipidemia in adulthood, as indicated by higher postprandial triglyceride (TG) levels in TPN-control (P<0.05), compared to SowFed. IUGR piglets were particularly sensitive to neonatal TPN feeding, as TPN-IUGR piglets developed obesity and dyslipidemia in adulthood, as indicated by greater backfat thickness (P<0.05), higher liver TG (P<0.05), slower postprandial TG clearance (P<0.05), and elevated fasting plasma non-high density lipoprotein (HDL)-cholesterol (P<0.01), and non-esterified fatty acids (NEFA) (P<0.001), compared to TPN-control. CONCLUSIONS: Feeding TPN in early life increases the risk of developing dyslipidemia in adulthood, especially in IUGR neonates; however, methyl nutrient supplementation to TPN did not prevent TPN-induced changes in lipid metabolism.

Low-carbon diets across diverse dietary patterns: Addressing population heterogeneity under constrained optimization.

Mathematical optimization is a useful tool for modeling diets that fulfill requirements for health and environmental sustainability, however, population-based optimization approaches fail to account for underlying dietary diversity in populations. This study proposes a methodological approach to consider diverse dietary intake patterns in mathematical optimization of nutritionally adequate low-carbon diets and investigates the differences between different population groups, along with trade-offs between greenhouse gas emission (GHGE) reduction and the inconvenience of dietary changes required to achieve optimized diets. A k-means clustering analysis was applied to individual dietary intake data from Denmark, which resulted in four clusters with different dietary patterns. This was followed by quadratic programming, wherein the total dietary changes required from the observed diet within each cluster were used as a proxy for consumer inconvenience (i.e., "inconvenience index") and were minimized while fulfilling nutrient constraints and incrementally tightened GHGE constraints. Across clusters, a steep increase of the inconvenience index was observed at GHGE levels below approximately 3 kg CO2e/10 MJ, corresponding to GHGE reductions of 24-36 % in different clusters. In all clusters, the optimized diets with nutritional and GHGE constraints showed common traits of increased content of cereals and starches, eggs, and fish and decreased amounts of beef and lamb, cheese, animal-based fats, and alcoholic beverages, but differences across clusters were also observed, maintaining characteristics of the clusters' baselines. When additional health-based targets for food amounts were applied as constraints, the optimized diets converged towards the same type of diet. The total inconvenience of dietary changes required to fulfill constraints differed between clusters, indicating that specific sub-populations may be more effective targets for dietary transition. The method has potential for future integration of more sustainability aspects and different consumer preferences.

Adaptive dynamic programming for containment control with robustness analysis to iterative error: A global Nash equilibrium solution.

Global Nash equilibrium is an optimal solution for each player in a graphical game. This paper proposes an iterative adaptive dynamic programming-based algorithm to solve the global Nash equilibrium solution for optimal containment control problem with robustness analysis to the iterative error. The containment control problem is transferred into the graphical game formulation. Sufficient conditions are given to decouple the Hamilton-Jacobi equations, which guarantee the solvability of the global Nash equilibrium solution. The iterative algorithm is designed to obtain the solution without any knowledge of system dynamics. Conditions of iterative error for global stability are given with rigorous proof. Compared with existing works, the design procedures of control gain and coupling strength are separated, which avoids trivial cases in the design procedure. The robustness analysis exactly quantifies the effect of the iterative error caused by various sources in engineering practice. The theoretical results are validated by two numerical examples with marginally stable and unstable dynamics of the leader.

Court Navigators and Opportunities for Disseminating Overdose Prevention Strategies.

Objective. Describe how navigator programs from three New England states (Connecticut, Maine, and Massachusetts) function to connect individuals using court services to community-based treatment and services. Hypothesis. Implementation science frameworks can be used to analyze how multiple court programs function to inform replication and address gaps in the use of overdose prevention strategies. Method. The Template for Intervention Description and Replication (TIDieR) Implementation informed semi-structured interviews as part of an exploratory mixed-methods analysis of court navigator programs. Analysis of interviews based on the TIDieR and administrative data on client contacts (n = 436) were analyzed. A subsample of clients with additional background information (n = 249) was used to examine court navigator recommendations. Results. The TIDieR revealed court navigator programs shared basic intervention characteristics but varied dramatically by resources and organization contexts. Nearly half (46.5%) of clients were self-referred and more than two-thirds (69.1%) approved follow-up but varied by program. Of those allowing follow-up, more than one-third (35.7%) had previously experienced an overdose. Court navigators were significantly more likely to refer clients with a history of overdose to locations where they can receive naloxone (the opioid overdose antidote) though only one program had naloxone on hand. Conclusion. Navigators are employed by community organizations with access to courthouses to provide resources to those passing through. These efforts can be implemented to support linkages to treatment and services but there is a significant gap in the allocation of overdose prevention strategies in court settings and court navigator programs have the potential to address this unmet need.

Effects of early-life amino acids supplementation on fish responses to a thermal challenge.

Nutritional programming is a promising concept for promoting metabolic adaptation of fish to challenging conditions, such as the increase in water temperature. The present work evaluates in ovo arginine or glutamine supplementation as enhancers of zebrafish metabolic or absorptive capacity, respectively, at optimum (28 ºC) and challenging temperatures (32 ºC) in the long-term. Growth performance, free amino acids profile, methylation index and the activity levels of digestive and intermediary metabolism enzymes were analysed to assess the metabolic plasticity induced by an early nutritional intervention. Temperature affected fish larvae growth performance. At the end of the experimental period 28 ºC-fish showed higher dry weight than 32 ºC-fish. The effects of the early supplementation were reflected in the larval free amino acids profile at the end of the experiment. Higher methylation potential was observed in the ARG-fish. In ovo amino acid supplementation modulated the metabolic response in zebrafish larvae, however, the magnitude of this effect differed according to the amino acid and the temperature. Overall, arginine supplementation enhanced carbohydrates metabolism at 32 ºC. In conclusion, the present work suggests that in ovo arginine supplementation may promote a better adaptive response to higher temperatures.

Water quality constrained adjustment planning for regional breeding management with nonlinear programming model under uncertainty in Wenchang City, China.

Basin water pollution caused by livestock, poultry and fish breeding is still a serious problem for remote villages, however, reliable regional breeding management programming have the potentials to improve pollution status. This paper focuses on the optimal model design and water quality analysis of the livestock, poultry and fish breeding system for Wenchang City, China. Methods of multi-objective programming (MOP), interval parameter programming (IPP), fuzzy-stochastic parameter programming (FSPP), and chance constrained programming (CCP) were incorporated into the developed model to tackle multi uncertainties described by interval values, probability distributions, fuzzy membership function. Based on the estimation of local breeding potential and current situation of surface water section, a multi-objective mixed fuzzy-stochastic nonlinear programming optimization model is presented with one-dimensional water quality model. In order to evaluate the environmental carrying capacity of livestock, poultry and fishery manure, predict its development trend and investigate the implementation effect of different emission reduction policies, this paper designs quantization system of the urban water environmental carrying capacity for the model. The results indicated that the water environment pollutant absorption capacity and carrying capacity of Wenchang city have approached the limit especially the towns in the northeast of City which limited the overall development space of the City. The modeling results are valuable for supporting the adjustment of the existing livestock, poultry and fish breeding schemes within a complicated system benefit and surface water quality situation under uncertainty.

Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks.

This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.

A new bi-level TOPSIS based neutrosophic programming technique for land allocation to medium farm holders.

Agriculture impacts a country's social and economic growth. Crop allocation, crop combinations, and crop production processes are all necessary to achieve optimal results during various growing seasons. To maximize farm earnings, proper farm planning and resource allocations are necessary. In agriculture, land allocation problems involve several uncertainties and unpredictable variables, includes water supply, labour demands, fertility use, and food requirements. The objective of this study is to propose novel bi-level programming approaches to overcome such issues and obtain optimal land allocation for medium-sized farmers. The current study examines a bi-level, TOPSIS-based neutrosophic programming approaches in two cases, including non-interactive and interactive approaches with linear, exponential, and hyperbolic membership functions to maximize net profit and minimize the expenditure. The proposed methods are compared to other approaches, such as the Torabi & Hassini approach, the Fuzzy Optimization Technique (FOT), and the Intuitionistic Fuzzy Optimization Technique (IFOT) and are found to be more effective than the existing ones.

An explainable model for predicting Worsening Heart Failure based on genetic programming.

Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96% average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations.

Let's not abandon programmed frozen embryo transfers yet: a countercurrent perspective.

The countercurrent opinion given in this paper is that the optimal management of frozen embryo transfers (FET) is not a one-size-fits-all matter, but rather one that should be decided after considering all the various parameters and options. This choice should notably encompass patients' individual characteristics - including variable risks of obstetric complications - and weigh out the respective advantages of each FET option in each case. While there may be real advantages for natural-cycle FET in many cases, these need to be balanced against both practical and clinical issues. Contrary to several prevailing, sometimes loudly expressed suggestions, there is not a one single effective approach when it comes to choosing a mode of scheduling FET.

Presaccadic preview shapes postsaccadic processing more where perception is poor.

The presaccadic preview of a peripheral target enhances the efficiency of its postsaccadic processing, termed the extrafoveal preview effect. Peripheral visual performance-and thus the quality of the preview-varies around the visual field, even at isoeccentric locations: It is better along the horizontal than vertical meridian and along the lower than upper vertical meridian. To investigate whether these polar angle asymmetries influence the preview effect, we asked human participants to preview four tilted gratings at the cardinals, until a central cue indicated which one to saccade to. During the saccade, the target orientation either remained or slightly changed (valid/invalid preview). After saccade landing, participants discriminated the orientation of the (briefly presented) second grating. Stimulus contrast was titrated with adaptive staircases to assess visual performance. Expectedly, valid previews increased participants' postsaccadic contrast sensitivity. This preview benefit, however, was inversely related to polar angle perceptual asymmetries; largest at the upper, and smallest at the horizontal meridian. This finding reveals that the visual system compensates for peripheral asymmetries when integrating information across saccades, by selectively assigning higher weights to the less-well perceived preview information. Our study supports the recent line of evidence showing that perceptual dynamics around saccades vary with eye movement direction.