Search Results (112)

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T₁-weighted, T₂-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T₁/T₂-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P₂ (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P₂ values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P₂ was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by irreversible cognitive decline and synaptic dysfunction and represents the most prevalent etiology of dementia, accounting for an estimated 60–70% of all clinically diagnosed cases worldwide. The growing focus on microglia–neuron interactions in AD research highlights their diverse, region-specific responses, which are driven by the functional and pathological heterogeneity across different brain regions. Therefore, investigating the interactions between microglia and neurons is of crucial importance. To explore the regional heterogeneity of microglia–neuron crosstalk in AD, we integrated human single-nucleus RNA sequencing data from the prefrontal cortex (PFC), hippocampus (HPC), and occipital lobe (OL) provided by the ssREAD database. Our study delineated four microglial subtypes and uncovered a pseudotime trajectory activation trajectory leading to the disease-associated microglia (DAM) phenotype. The transition along this trajectory is driven and stabilized by a key molecular switch: the coordinated downregulation of inhibitory factors (e.g., LINGO1) and upregulation of immune-effector and antigen-presentation programs, which collectively establish the pro-inflammatory DAM state. Furthermore, we observed that each brain region displayed unique microglia–neuron communication patterns in response to AD pathology. The PFC and OL engage a THY1-ITGAX/ITGB2 signaling axis; the HPC predominantly utilizes the PTPRM pathway. Notably, THY1 dysregulation strongly correlates with pathology in the PFC, HPC, and OL, suggesting that microglia–neuron crosstalk in AD possesses both heterogeneity and commonality. The main contribution of this study is the systematic characterization of region-specific microglia-neuron interactions and the identification of THY1 as a potential mediator that may be targeted therapeutically to modulate microglial function in affected brain regions. Full article

The increasing environmental impact of the textile industry has led to the development of sustainable production methods. One of the effective approaches is the use of recycled fibers, which helps to save resources, reduce carbon emissions, and support the circular economy. This study investigates the feasibility of producing durable upholstery fabrics incorporating recycled polypropylene (r-PP) and virgin polypropylene (v-PP). Filament yarns with varying r-PP/v-PP blend ratios, produced by the melt spinning process, were used as weft yarns, while commercially available virgin polyester filament yarns were employed in the warp direction for all fabric samples. Performance tests in accordance with the standards were applied to the fabrics and the results were also evaluated statistically. The results show that acceptable performance is achieved in some mechanical properties if similar blend ratios and production parameters are used. In the study, an optimization model was developed to maximize the weft breaking strength using the equations obtained from the regression analyses. With the help of the mathematical model created, the values of other physical and performance properties of the fabric depending on the maximum breaking strength value could be estimated without the need for trial production. The model was solved using Lingo 18.0 optimization software. The solution of the model revealed that the optimum weft yarn blend ratio is 10/90 r-PP/v-PP, and the maximum weft breaking strength value is 562.45 N. Full article

Water removal is crucial in natural gas processing to minimize water content, ensure safe transmission, and prevent operational issues like equipment corrosion and hydrate formation. Glycol absorption could be considered as one of the most effective methods used for natural gas dehydration and dew point control. However, during solvent regeneration, some pollutants, like benzene, toluene, ethylbenzene, and xylene (BTEX), are released to the atmosphere, resulting in catastrophic physical and mental health problems. Minimizing such pollutants that have negative impacts is highly needed to avoid the related negative environmental consequences. The objective of the current work is to investigate alternative strategies targeted to minimize BTEX emissions and guarantee efficient control of the dew point. Two strategies are introduced and investigated in this work; the first strategy is based on revamping an existing unit by adding a new cooler upstream glycol inlet separator, while the second strategy is based on using alternative glycols. The proposed strategies are applied to an Egyptian natural gas dehydration unit to select the optimum scenario that achieves the minimum BTEX emissions with efficient dew point control. It is found that natural gas dehydration using monoethylene glycol (MEG) is the best scenario in reducing BTEX emissions with efficient dew point control. The impact of operating conditions on BTEX emissions, along with natural gas water content, is also investigated. Lingo optimization software, v. 18, as well as HYSYS, v. 14, are used to find the optimum operating conditions for efficient dew point control with minimum BTEX emissions. It is demonstrated that stripping gas, MEG circulation rate, and inlet feed gas temperature have remarkable effects on BTEX emissions. Two quadratic correlations are also introduced in this study to efficiently relate BTEX emissions and water dew point to the influencing operating conditions. Full article

Collagens comprise a large, diverse family of proteins that are abundantly expressed throughout most tissues. As a main component of the extracellular matrix (ECM), it is becoming increasingly appreciated how vital collagens are to tumor development, progression, and metastasis. COL6A3, which encodes the alpha 3 chain of type VI collagen, is a unique member of the collagen family that encodes a C-terminal peptide with powerful signaling capabilities, named endotrophin (ETP). Expression of COL6A3 is required for the survival, migration, and invasion of many cancer cell lines, including breast, bladder, liver, and colorectal cancers. ETP, which was originally discovered to be enriched in the adipocytes of mammary tumors, is a powerful oncopeptide that can alter signaling of tumor and stromal cells. ETP has greater signaling potential than the parental COL6A3 as it can induce EMT and promote chemoresistance, metastasis, and stemness in an TGF-β-like manner. ETP can also function independently of TGF-β to recruit endothelial cells and macrophages. In this review, we examine the molecular implications of COL6A3 and ETP expression and their effects on tumor growth, metastasis, and therapeutic response. Finally, we speculate on the potential of serum ETP as a prognostic biomarker in both carcinomas and sarcomas. In summary, COL6A3 and ETP are powerful drivers of tumor growth that have potential as noninvasive diagnostic and prognostic tools for the clinical management of cancer. Full article

Prenatal tobacco smoke exposure (TSE) has been associated with significant alterations in DNA methylation (DNAm), an epigenetic mechanism with potential functional consequences to child development. This pilot study aimed to investigate differential DNAm patterns in preterm children with and without prenatal TSE using reduced representation bisulfite sequencing (RRBS) to interrogate a wider array of sites than in more common approaches, namely microarrays. Buccal swabs were collected from 16 two-year-old children (7 with TSE, 9 without), and DNAm was quantified at over 1.3 million CpG sites. To identify differential DNAm, univariable analyses were first performed and followed by Bayesian beta-binomial hierarchical regression models for sequence count data including adjustment for potential confounders. False Discovery Rate correction was used to account for multiple comparisons. Significant differential methylation was observed at CpG sites within intronic regions of the CALN1 and LINGO1 genes and the distal intergenic region of the TBL1XR1 gene. These findings suggest that prenatal TSE may influence epigenetic regulation in genes involved in neurodevelopment. This study demonstrates the importance of RRBS in identifying novel DNAm changes associated with prenatal TSE and highlights the need for larger studies to validate and expand upon these preliminary findings. Full article

Background: Healthcare systems are increasingly strained by limited operating room resources and rising demand, a situation intensified by the COVID-19 pandemic. These pressures have resulted in overcrowded surgical departments, prolonged waiting times for elective procedures, worsened patient health outcomes, and increased hospital expenditure costs. Methods: To address these challenges, this study proposes a multi-objective mathematical optimization model as the analytical core of a decision support approach for OR resource allocation. The model considers multiple constrained resources, including OR time, intensive care units, medium care units, and nursing staff, and aims to minimize both elective patients’ waiting times and total incurred costs over a one-week planning horizon. Developed using real hospital data from a large facility in Thailand, the model was implemented in LINGO version 16.0, and a sensitivity analysis was conducted to assess the impact of surgical department priorities and overtime allowances. Results: Compared to current practices, the optimized OR schedule reduced average waiting times by approximately 7% and total costs by 5%, while balancing resource utilization. Conclusions: This study provides a data-driven tool to support hospital resource planning, improve OR efficiency, and respond effectively to future healthcare crises. Full article

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are a deadly subtype of soft tissue sarcoma for which effective therapeutic options are lacking. Currently, the best treatment for MPNSTs is complete surgical resection with wide negative margins, but this is often complicated by the tumor size and location and/or the presence of metastases. Radiation or chemotherapy may be combined with surgery, but patient responses are poor. Targeted treatments, including small-molecule inhibitors of oncogenic proteins such as mitogen-activated protein kinase kinase (MEK), cyclin-dependent kinases 4 and 6 (CDK4/6), and Src-homology 2 domain-containing phosphatase 2 (SHP2), are promising therapeutics for MPNSTs, especially when combined together, but they have yet to gain approval. Immunotherapeutic approaches have been revolutionary for the treatment of some other cancers, but their utility as single agents in sarcoma is limited and not approved for MPNSTs. The immunosuppressive niche of MPNSTs is thought to confer inherent treatment resistance, particularly to immunotherapies. Remodeling an inherently “cold” tumor microenvironment into a “hot” immune milieu to bolster the anti-tumor activity of immunotherapies is of great interest throughout the cancer community. This review focuses on novel therapeutics that target dysregulated factors and pathways in MPNSTs, as well as different types of immunotherapies currently under investigation for this disease. We also consider how certain therapeutics may be combined to remodel the MPNST immune microenvironment and thereby generate a durable anti-tumor immune response to immunotherapy. Full article

Conventional ammonia production uses fossil-based hydrogen, resulting in high greenhouse gas emissions. Given the growing demand for sustainable solutions, it is essential to replace fossil hydrogen with renewable alternatives. This study assessed the technical, economic, and environmental viability of renewable ammonia production in Minas Gerais. To this end, an optimization model based on mixed integer linear programming (MILP) was developed and implemented in LINGO 20^® software. The model incorporated investment costs; raw materials; transportation; emissions; and indicators such as NPV, payback, and minimum sale price. Hydrogen production routes integrated into the Haber–Bosch process were analyzed: biomass gasification (GS_WGS), anaerobic digestion of vinasse (Vinasse_BD_SMR), ethanol reforming (Ethanol_ESR), and electrolysis (PEM_electrolysis). Vinasse_BD_SMR showed the lowest costs and the greatest economic viability, with a payback of just 2 years, due to the use of vinasse waste as a raw material. In contrast, the electrolysis-based route had the longest payback time (8 years), mainly due to the high cost of the electrolyzers. The substitution of conventional hydrogen made it possible to avoid 580,000 t CO₂ eq/year for a plant capacity of 200,000 t NH₃/year, which represents 13% of the Brazilian emissions from the nitrogenated fertilizer sector. It can be concluded that the viability of renewable ammonia depends on the choice of hydrogen source and logistical optimization and is essential for reducing emissions at large scale. Full article

The development of new technologies for the production of renewable energy is fundamental to reducing greenhouse gas emissions. Therefore, the search for new energy generation methods that are environmentally responsible, socially rational, and economically viable is gaining momentum in order to mitigate carbon footprint. The aviation sector is responsible for a significant fraction of greenhouse gas emissions; for this reason, the decarbonisation of this sector must be investigated using biorefinery models. This study presents a mixed-integer linear programming (MILP) model for optimising the design and configuration of the supply chain in different states of Brazil for the production of sustainable aviation fuel (SAF) and green diesel and gasoline, using microalgae cultivated in sugarcane vinasse as the raw material. The technology of hydrothermal liquefaction was assessed in terms of its capacity to convert microalgae without need for the energy-intensive drying step. The MILP model was developed in the LINGO v.20 software using a library of physical and economic process models. We consider the selection of processes based on the object of total minimum cost, with optimal production plant scaling and regional supply chain design, including an assessment of resources and final product distribution. A case study was implemented in Brazil, considering different regions of the country and its local demands for fuels. São Paulo is the most profitable state, with a cash flow of 1071.09 and an IRR of 36.19%, far outperforming the rest. Transport emissions alone represent between 0.6 and 8.6% of emissions generated by the model. The costs of raw materials, mainly hydrogen (57%) and electricity (27%) represent the main costs evaluated in the model. The production cost (MUS$/TJ biofuel) is in the range of 0.009–0.011. Finally, changes in the cost of electricity have the greatest impact on the model. Full article

Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO₂, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions. Full article

The optimal management of pension funds has become increasingly critical. As the population ages, the effective management of pension funds is essential for the social security system. The primary goal of this paper is to develop a deterministic nonlinear multi-objective optimization model to determine the contribution rates in a defined contribution pension system. The computational optimization model was implemented using the LINGO language. In the first part of this study, three main scenarios were analyzed considering different inflation rates, focusing on the objective function that minimizes the salary percentages workers pay when saving for a specified period while aiming to achieve a certain number of coverage years. The first scenario assumes that the worker desires an economic quality equivalent to their working life, showing that contribution rates range from 10% to 30% (with a 3% inflation rate). The second scenario posits that the worker only requires 80% of their equivalent salary during retirement, resulting in contribution rates directly proportional to those in scenario 1 (using the same parameters). The third scenario speculates that inflation may reach 7% per year, causing contribution rates to rise significantly from 40% to 80%. Finally, the Pareto front illustrates the trade-off between the contribution rate and the coverage years based on scenario 1 parameters. Full article

Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using integer programming algorithms. This study explores various integer programming techniques and their effectiveness in optimising complex scheduling requirements in higher education institutions. We analysed 95 integer programming-based models developed for solving university timetabling problems, covering relevant research from 1990 to 2023. The goal is to provide insights into the evolution of these algorithms and their impact on improving university scheduling. We identify that the implementation rate of models using integer programming is 98%, which is much higher than 34% implementation rates using meta-heuristics algorithms from the existing review. The integer programming models are analysed by the problem types, solutions, tools, and datasets. For three types of timetabling problems including course timetabling, class timetabling, and exam timetabling, we dive deeper into the commercial solvers CPLEX (47), Gurobi (11), Lingo (5), Open Solver (4), C++ GLPK (4), AIMMS (2), GAMS (2), XPRESS (2), CELCAT (1), AMPL (1), and Google OR-Tools CP-SAT (1) and identify that CPLEX is the most frequently used integer programming solver. We explored the uses of machine learning algorithms and the hybrid solutions of combining the integer programming and machine learning algorithms in higher education timetabling solutions. We also identify areas for future work, which includes an emphasis on using integer programming algorithms in other industrial areas, and using machine learning models for university timetabling to allow data-driven solutions. Full article

(1) Background: Buffaloes are crucial livestock species for food and service in tropical and subtropical regions. Buffalo genetics, particularly in indigenous Chinese breeds such as the Xiangxi white buffalo (XWB), remains an intriguing area of study due to its unique traits and regional significance. (2) Methods: This investigation utilized the whole-genome sequences of twenty XWBs (newly sequenced), along with eighty published whole-genome sequences of other buffalo breeds (including Guizhou white buffalo, river buffalo, and Chinese buffalo in the Yangtze River). Using whole-genome sequencing analysis technology, the population structure, genomic diversity, and selection signatures of XWB were determined. (3) Results: This study revealed that the XWB, being phylogenetically positioned in the middle and lower reaches of the Yangtze River, exhibited substantial genomic diversity. Employing four selection sweep detection methods (CLR, iHS, π-ratio, and F_ST), several genes were positively identified for adaptive traits in the XWB, including coat color phenotypes (ASIP, KIT), the nervous system (GRIK2), reproduction (KCNIP4), growth and development (IFNAR1, BMP6, HDAC9, MGAT4C, and SLC30A9), the body (LINGO2, LYN, and FLI1), immunity (IRAK3 and MZB1), and lactation (TP63, LPIN1, SAE1). (4) Conclusions: In conclusion, this study enhances our understanding of the genetic distinctiveness and adaptive traits of XWB, highlighting selection signatures crucial for future breeding and conservation and ensuring sustainable use of this vital livestock resource. Full article

Biodiesel is a promising alternative fuel that represents a sustainable and environmentally friendly energy source. Due to its complete carbon cycle, it reduces dependence on fossil fuels and lowers greenhouse gas emissions. However, the use of biodiesel in diesel engines is associated with several challenges, including an increase in nitrogen oxide and particulate emissions, incompatibility with cold climates, and lower calorific value. By using nanoparticles as fuel additives, there is a potential to improve the properties of biodiesel and address its shortcomings. In this work, the characteristics of biodiesel derived from waste cooking oil have been enhanced using nanoparticle additives, which result in the usage of a higher percentage of the biodiesel in diesel engines. Nanoparticles of cerium oxide, silicon dioxide, and aluminum oxide have been investigated in different concentrations as biodiesel additives. Two mathematical models are introduced in this work and solved by LINGO optimization software (version 18); the first one seeks to predict the characteristics of biodiesel with nanoparticles in any blend of diesel–biodiesel–nanoparticles, while the second model aims to maximize the biodiesel ratio in a biodiesel–diesel–nanoparticles blend. The application of the combined two models aids in the selection of the optimal nanomaterial that improves the properties of biodiesel and permits an increase in the biodiesel mixing ratio in the fuel. The results show that the best nanoparticle type is cerium oxide at a concentration of 100 ppm, and the optimal mixing ratio of biodiesel blended with CeO₂ nanoparticles is 24.892%. An unmodified diesel engine is operated and evaluated with the optimum blend (24.892% biodiesel + 75.108% petrol diesel + 100 ppm CeO₂ nanoparticles). It is found that significant improvements in engine performance and emissions compared with the conventional diesel are achieved. The reductions in brake-specific fuel consumption (BSFC), smoke opacity, and carbon monoxide emissions are 24%, 52%, and 30%, respectively. Full article