Optimal Function Research Articles

Physical constraints on the positions and dimensions of the zebrafish swim bladder by surrounding bones

AbstractPrecise regulation of organ size and position is crucial for optimal organ function. Since the swim bladder is primarily responsible for buoyancy in teleosts, early development and subsequent inflation of the swim bladder should be appropriately controlled with the body growth. However, the underlying mechanism remains unclear. In this study, we show that the size and position of the swim bladder are physically constrained by the surrounding bones in zebrafish. Non‐invasive micro‐CT scanning revealed that the anterior edge of the swim bladder is largely attached to the os suspensorium, which is an ossicle extending medioventrally from the 4th centrum. Additionally, we observed that hoxc6a mutants, which lack the os suspensorium, exhibited an anterior projection of the swim bladder beyond the 4th vertebra. During the swim bladder development, we found that the counterclockwise rotation of the os suspensorium correlates with posterior regression of the swim bladder, suggesting that the os suspensorium pushes the swim bladder posteriorly into its proper position. Furthermore, our results revealed a close association between the posterior region of the swim bladder and the pleural ribs. In hoxaa cluster mutants with additional ribs, the swim bladder expanded posteriorly, accompanied by an enlarged body cavity. Taken together, our results demonstrate the importance of the surrounding bones in the robust regulation of swim bladder size and position in zebrafish.

Protein Design by Integrating Machine Learning and Quantum-Encoded Optimization

The protein design problem involves finding polypeptide sequences folding into a given three-dimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine-learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design. Published by the American Physical Society 2024

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into four stages: Non-demented (ND), moderate demented (MoD), mild demented (MiD), and very mild demented (VMD). To prepare the raw dataset for analysis, the collected magnetic resonance imaging (MRI) images are subjected to several pre-processing techniques in order to improve the performance accuracy of the proposed model. Medical images generally have poor contrast and get affected by noise, which ends up with inaccurate diagnosis. For the different phases of AD to be detected, a clear image is necessary. To address this issue, the influence of the artefacts must be reduced, enhance the contrast, and reduce the loss of information. A novel framework for image enhancement is suggested to increase the accuracy in the detection and identification of AD. In this study, the raw MRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) database is subjected to skull stripping, contrast enhancement, and image filtering followed by data augmentation to balance the dataset with four types of Alzheimer's classes. The pre-processed data are subjected to five different pre-trained models such as AlexNet, ResNet, VGG 16, EfficientNet, and Inceptionv3 achieving a testing accuracy rate of 91.2%, 88.21%, 92.34%, 93.45%, and 85.12%, respectively. These pre-trained models are compared with the proposed CNN (convolutional neural network) model designed with Adam optimizer and Flatten Swish activation function which reaches the highest accuracy of 96.5% with a learning rate of 0.000001. The five pre-trained CNNmodels along with the proposed swish-based AD-CNN were tested using various performance metrics to evaluate the model efficiency in classifying and identifying the AD classes. From the result analysis, it is evident that the proposed AD-CNN model outperforms all the other models.

Digital Architectural Form Generation Through Pixel System-Driven Image Feature Information

This study analyzes valuable information (image feature information) from pixel-based images to systematize the form generation process utilizing this information. Information in architecture is mainly used as an analytical tool for functional design optimization. However, the Fourth Industrial Revolution and advances in information and communication technology have positioned information as a core value-creating tool. Consequently, there is a growing need to develop methodologies for effectively applying information to architectural design generation. This study combines digital technology with images, which directly influence architectural design, transforming them into numerical information. By systematizing the three-dimensional form generation process using low-level image feature information, it analyzes the relationship between the characteristics of valuable information and the generated forms. This approach helps identify the interrelationships between the characteristics of valuable information and the generated forms, the complementarity needed to mitigate the limitations of valuable information, and the potential for architectural applications of the three-dimensional form generation methodology utilizing image information. This approach reduces labor and enhances architectural aesthetics by instantly generating diverse early design alternatives. Such research can provide a more logical basis for avoiding intuitive judgments during the design decision-making process. It can also lay the groundwork for information-centric design systems in architecture.

NMR Phase Error Correction with New Modelling Approaches

Nuclear Magnetic Resonance (NMR) spectroscopy is a highly sensitive analytical technique essential for precise molecular identification and quantification. However, accurate results depend on effective pre-processing to correct for various types of errors. Phase error correction, in particular, is crucial for ensuring the reliability of NMR data. Current methods often rely on a single linear model, which may not adequately address all types of phase errors. As a result, this limitation frequently requires manual intervention, making the process both time-consuming and prone to errors. To address these limitations, we propose three modelling approaches for NMR phase error correction: nonlinear shrinkage, multiple models, and a new optimization function called delta absolute net minimization (DANM). Our comparison of seven methods revealed that nonlinear shrinkage outperformed others in both simulated spectra and a diabetes study, followed by multiple models with DANM. Additionally, our spike-in experiments demonstrated that DANM performed quite well in both single and multiple models. Our nonlinear shrinkage approach is a simple yet effective solution. We provide an open-source R package, NMRphasing, available on CRAN (https://cran.r-project.org/web/packages/NMRphasing/) and on GitHub (https://github.com/ajiangsfu/NMRphasing).

Energy efficient mobile sink driven data collection in wireless sensor network with nonuniform data.

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink's traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

Optimal functioning after early mild traumatic brain injury: Evolution and predictors.

Early mild traumatic brain injury (mTBI or concussion sustained between 0 and 5 years old) can lead to post-concussive symptoms, behavioral changes, and cognitive difficulties. Although school-age children (6-17 years old) experience similar consequences, severe neuropsychological deficits are not common, and the majority have no persisting symptoms after one month. Thus, there may be value in focusing on what characterizes optimal functioning (or wellness) after mTBI, but this has not been explored in young children. This study documents the evolution and predictors of optimal functioning after early mTBI. Participants were 190 children aged 18 - 60 months with mTBI (n = 69), orthopedic injury (OI; n = 50), or typical development (TDC; n = 71). Optimal functioning was defined as: (1) no clinically significant behavioral problems; (2) no cognitive difficulties; (3) no persisting post-concussive symptoms; (4) average quality of life or better. Predictors related to sociodemographic, injury, child, and caregiver characteristics included number of acute symptoms, child sex, age, temperament, maternal education, parent-child attachment and interaction quality, and parenting stress. Fewer children with mTBI had optimal functioning over 6 and 18-months post-injury compared to those with OI and TDC. Higher parent-child interaction quality and lower child negative affectivity temperament independently predicted optimal functioning. Children who sustain early mTBI are less likely to exhibit optimal functioning thantheir peers in the long-term. Parent-child interaction quality could be a potential intervention target for promoting optimal function.

Improved Information Exchange as an Advanced Governance Tool

Whereas many organisations focus on the optimal functioning of their processes to achieve their goals, we nowadays see organisational challenges spreading to their functioning within networks. This poses a significant question for management and leadership: Are their decisions shaping or controlling their own or the network partners’ functioning? However, modern networks grant partners autonomy to make their choices, believing that the collective outcome of these autonomous decision processes can also yield results. However, cooperative decision-making between network partners is pivotal in today's society. Finding a balance between control over the network, autonomy of the partners and cooperation between them leads to what is often referred to as the emergent behaviour of the network. So, 'context is leading' requires reacting fast and efficiently to become a resilient organisational network. It is becoming evident that the resilience within network functioning lies in the exchange of information and data availability. In current practice, partners often seek solutions by striving for unity of language and/or data dictionaries. Unfortunately, this approach doesn’t lead to optimal results in day-to-day practice. We propagate the adoption of a different approach. This article presents a non-invasive perspective on micro-contexts and their emerging information systems. By engaging these systems in their current form through formalised abstractions, we suggest effectively linking information and data sources to enhance information exchange and data availability. Improving information exchange will show itself instantly as an advanced governance tool

MDD-DETR: Lightweight Detection Algorithm for Printed Circuit Board Minor Defects

PCBs (printed circuit boards) are the core components of modern electronic devices, and inspecting them for defects will have a direct impact on the performance, reliability and cost of the product. However, the performance of current detection algorithms in identifying minor PCB defects (e.g., mouse bite and spur) still requires improvement. This paper presents the MDD-DETR algorithm for detecting minor defects in PCBs. The backbone network, MDDNet, is used to efficiently extract features while significantly reducing the number of parameters. Simultaneously, the HiLo attention mechanism captures both high- and low-frequency features, transmitting a broader range of gradient information to the neck. Additionally, the proposed SOEP neck network effectively fuses scale features, particularly those rich in small targets, while INM-IoU loss function optimization enables more effective distinction between defects and background, further improving detection accuracy. Experimental results on the PCB_DATASET show that MDD-DETR achieves a 99.3% mAP, outperforming RT-DETR by 2.0% and reducing parameters by 32.3%, thus effectively addressing the challenges of detecting minor PCB defects.

Iron Deficiency in Collegiate Athletes Obtaining Preparticipation Hemoglobinopathy Screening in the Upper Midwest.

Iron and other biologically important metals are essential to mitochondrial function but are not routinely evaluated. Their equilibrium is critical to the optimal performance of cells with high metabolic activity such as neurons, cardiomyocytes, and skeletal myocytes. Teenagers are at a high risk of iron deficiency even without anemia. Metal ion imbalances can cause cognitive impairments, muscle weakness, and sudden cardiac death. We aim to assess the current prevalence of iron deficiency among collegiate athletes in the Upper Midwest. Our study is a multicenter, retrospective chart review of outpatient clinics in a regional healthcare system between January 2012 and December 2023, and a national public database between 2017 and March 2020. We reviewed the ferritin concentrations of regional collegiate athletes having preparticipation sport evaluations and nationally in the NHANES database. We identified 643 unique individuals aged 16-21 years with 253 having ferritin screening. Iron deficiency (ferritin <20mcg/L) was present in 24.5% and hypoferritinemia (ferritin <50mcg/L) was present in 66.7% of collegiate athletes. From the NHANES database, 12.7% of active sampled participants aged 16-21 years were iron deficient. Our study findings suggest the need for universal screening for iron deficiency among collegiate athletes given the high prevalence of iron deficiency in both the retrospective chart review and NHANES database analysis. Given the critical role of metal ion homeostasis to optimal mitochondrial function, these findings may warrant the inclusion of ferritin testing in cardiac, neurological, and skeletal muscle evaluations.

Nonlinear Dynamics of an Electromagnetically Actuated Cantilever Beam Under Harmonic External Excitation

The present work is devoted to the study of nonlinear vibrations of an electromagnetically actuated cantilever beam subject to harmonic external excitation. The soft actuator that controls the vibratory motion of such components of a robotic structure led to a strongly nonlinear governing differential equation, which was solved in this work by using a highly accurate technique, namely the Optimal Auxiliary Functions Method. Comparisons between the results obtained using our original approach with those of numerical integration show the efficiency and reliability of our procedure, which can be applied to give an explicit analytical approximate solution in two cases: the nonresonant case and the nearly primary resonance. Our technique is effective, simple, easy to use, and very accurate by means of only the first iteration. On the other hand, we present an analysis of the local stability of the model using Routh–Hurwitz criteria and the eigenvalues of the Jacobian matrix. Global stability is analyzed by means of Lyapunov’s direct method and LaSalle’s invariance principle. For the first time, the Lyapunov function depends on the approximate solution obtained using OAFM. Also, Pontryagin’s principle with respect to the control variable is applied in the construction of the Lyapunov function.

Crude Oil Biodegradation by a Biosurfactant-Producing Bacterial Consortium in High-Salinity Soil

Bioremediation is a promising strategy to remove crude oil contaminants. However, limited studies explored the potential of bacterial consortia on crude oil biodegradation in high salinity soil. In this study, four halotolerant strains (Pseudoxanthomonas sp. S1-2, Bacillus sp. S2-A, Dietzia sp. CN-3, and Acinetobacter sp. HC8-3S), with strong environmental tolerance (temperature, pH, and salinity), distinctive crude oil degradation, and beneficial biosurfactant production, were combined to construct a bacterial consortium. The inoculation of the consortium successfully degraded 97.1% of total petroleum hydrocarbons in 10 days, with notable removal of alkanes, cycloalkanes, branched alkanes, and aromatic hydrocarbons. Functional optimization showed that this consortium degraded crude oil effectively in a broad range of temperature (20–37 °C), pH (6–9), and salinity (0–100 g/L). In salt-enriched crude-oil-contaminated soil microcosms, the simultaneous treatment of bioaugmentation and biostimulation achieved the highest crude oil degradation rate of 568.6 mg/kg/d, compared to treatments involving abiotic factors, natural attenuation, biostimulation, and bioaugmentation after 60 days. Real-time PCR targeting the 16S rRNA and alkB genes showed the good adaptability and stability of this consortium. The degradation property of the constructed bacterial consortium and the engineered consortium strategy may have potential use in the bioremediation of crude oil pollution in high-salinity soil.

Valley Path Planning on 3D Terrains Using NSGA-II Algorithm

Valley path planning on 3D terrains holds significant importance in navigating and understanding complex landscapes. This specialized form of path planning focuses on finding optimal routes that adhere to the natural contours of valleys within three-dimensional terrains. The significance of valley path planning lies in its ability to address specific challenges presented by valleys, such as varying depths, steep slopes, and potential obstacles. By following the natural flow of valleys, path planning can enhance the efficiency of navigation and minimize the risk of encountering difficult terrain or hazards. In recent years, an increasing number of researchers have focused on the study of valley path planning on 3D terrains. This study presents a valley path planning method utilizing the NSGA-II (Non-dominated Sorting Genetic Algorithm II) approach. To ensure that the paths generated by the algorithm closely follow the valley lines, the algorithm establishes an optimization function that includes three optimization criteria: mean altitude, flight route length, and mean offset. To test the performance of this algorithm, we conducted experiments based on workspaces based on three datasets full of 3D terrains and compared it with three baseline algorithms. The evaluation indicates that the suggested algorithm successfully designs routes that closely follow the valley contours.

Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions

This paper introduces the Group Forward–Backward Orthogonal Matching Pursuit (Group-FoBa-OMP) algorithm, a novel approach for sparse feature selection. The core innovations of this algorithm include (1) an integrated backward elimination process to correct earlier misidentified groups; (2) a versatile convex smooth model that generalizes previous research; (3) the strategic use of gradient information to expedite the group selection phase; and (4) a theoretical validation of its performance in terms of support set recovery, variable estimation accuracy, and objective function optimization. These advancements are supported by experimental evidence from both synthetic and real-world data, demonstrating the algorithm’s effectiveness.

The Impact of Diet and Nutrition on Immune Function: Implications for Health and Disease

The relationship between diet, nutrition, and immune function is crucial for maintaining health and preventing disease. This review explores how key nutrients and dietary patterns influence immune responses, highlighting the role of vitamins, minerals, and macronutrients in supporting both innate and adaptive immunity. Micronutrients such as vitamins C, D, A, and minerals like zinc, selenium, and iron are essential for the production of immune cells, antibodies, and regulating inflammation. Macronutrients, including proteins and omega-3 fatty acids, contribute to the immune system by supporting the synthesis of immune cells and modulating inflammatory responses. Dietary patterns also significantly affect immune function. The Mediterranean diet, rich in antioxidants, healthy fats, and fiber, enhances immune regulation and reduces chronic inflammation, while the Western diet, characterized by high sugar, refined carbohydrates, and unhealthy fats, can suppress immune activity and promote inflammation, increasing susceptibility to infections and chronic diseases. Malnutrition, whether through undernutrition or overnutrition, impairs immune function, increasing vulnerability to infections and hindering recovery. Gut health, closely linked to nutrition, also plays a vital role in immune regulation through the gut microbiome. This review underscores the importance of a balanced, nutrient-rich diet in supporting optimal immune function and preventing disease, with implications for both individual health and broader public health strategies. Keywords: Immune function, Micronutrients, Dietary patterns, Chronic inflammation, Disease prevention

Evaluating the roles of food matrix, lipid micronutrients and bioactives in controlling postprandial hypertriglyceridaemia and inflammation.

Lipids play an important role in human nutrition. Although adequate lipid consumption is necessary for an optimal functioning of the human body, overconsumption of saturated fatty acids can lead to postprandial hypertriglyceridaemia, which triggers the development of atherosclerosis. Important parameters that impact postprandial lipaemia and inflammation are related to the matrix structure and the fat-soluble micronutrient profile of ingested foods/lipids, but the specific effect of these parameters should be further studied, as most of the available studies evaluate their effect at fasting state. This review specifically explores the effects of food structure and fat-soluble micronutrients, from either micronutrient-rich foods or supplements, on postprandial hypertriglyceridaemia and inflammation. The review also highlights the potential of emerging biomarkers such as miRNAs or circulating microvesicles, as an alternative to the widely use biomarkers (e.g. low-density lipoproteins or blood concentration of pro-inflammatory cytokines), to identify inflammation associated with postprandial hypertriglyceridaemia at early stages.

Performance Analysis and Optimization of Amazon Web Service Lambda Functions in Serverless Systems

Performance Analysis and Optimization of Amazon Web Service Lambda Functions in Serverless Systems

Deephive: A Reinforcement Learning Approach for Automated Discovery of Swarm-Based Optimization Policies

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each particle within the swarm is defined as its current position and function value within a design space, and the particles learn to take favorable actions that maximize the reward, which is based on the final value of the objective function. The proposed approach is tested on 50 benchmark optimization functions and compared to the performance of other global optimization strategies. Furthermore, the generalization capabilities of the trained particles on the four categories of optimization benchmark functions are investigated. The results show superior performance compared to the other optimizers, desired scaling when the dimension of the functions is varied, and acceptable performance even when applied to unseen functions. On a broader scale, the results show promise for the rapid development of domain-specific optimizers.

Design and Test of a Traction Side-Pull Square Straw Bale Pick-Up-and-Stack Truck

To address the issues of limited one-time loading capacity, single functionality, and low automation level in existing square straw bale pickers, a large automated square straw bale pick-up-and-stack truck that integrates picking, stacking, transporting, and bundling functions has been developed, combining the technical advantages of one-time field removal and storage of bales. We innovatively designed a side-pulling traction mechanism that can realize the rapid transition between the transporting state and working state of the machine; a picking device that can complete the continuous action of forking, lifting, turning positioning, and de-forking; and a bundling device that can realize the adjustment of the attitude of square straw bales. Response surface tests were conducted on the prototype to determine the key structural and operational parameters, using the bundle completion rate and regular bale rate as evaluation indicators. Regression and significance tests were performed on the machine’s forward speed, chassis frame offset, and the ground clearance of the fork tine to determine the influence and priority of these factors on the evaluation indicators. Through multi-objective function optimization of the regression model, the optimal parameter combination was found to be a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, resulting in a bundle completion rate of 98.85% and a regular bale rate of 96.96%. Subsequent field tests with the optimized parameters showed that at a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, the bundle completion rate was 98.37% and the regular bale rate was 95.83%, meeting the relevant design requirements. This study can provide a reference for the design and development of straw collection and storage machinery.

Mitochondrial quality control in alcohol-associated liver disease.

Excessive alcohol consumption is a leading cause of alcohol-associated liver disease (ALD), a significant global health concern with limited therapeutic options. Understanding the key factors contributing to ALD pathogenesis is crucial for identifying potential therapeutic targets. Central to ALD pathogenesis is the intricate interplay between alcohol metabolism and cellular processes, particularly involving mitochondria. Mitochondria are essential organelles in the liver, critical for energy production and metabolic functions. However, they are particularly vulnerable to alcohol-induced damage due to their involvement in alcohol metabolism. Alcohol disrupts mitochondrial function, impairing ATP production and triggering oxidative stress, which leads to cellular damage and inflammation. Mitochondrial quality control mechanisms, including biogenesis, dynamics, and mitophagy, are crucial for maintaining optimal mitochondrial function. Chronic alcohol consumption disrupts mitochondrial quality control checkpoints, leading to mitochondrial dysfunction that impairs fatty acid oxidation and contributes to hepatic steatosis in ALD. Moreover, alcohol promotes the accumulation of damaged mitochondria and the release of proinflammatory components, exacerbating liver damage and inflammation. Preserving mitochondrial health presents a promising therapeutic approach to mitigate ALD progression. In this review, we provide a comprehensive overview of the effects of alcohol on mitochondrial function and quality control mechanisms, highlighting their role in ALD pathogenesis. Understanding these mechanisms may pave the way for the development of novel therapeutic interventions for ALD.