Reliable Approach Research Articles

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.

Integration of a needle valve mechanism with cura slicing software for improved retraction in pellet-based material extrusion

Fused granular fabrication (FGF) is a cost-effective and increasingly popular additive technology that enables the production of parts using a wide range of exotic materials that are not typically available with material extrusion additive manufacturing. This paper presents a novel needle valve approach for flow control in a FGF system that uses flexibly connected screw extruders. The addition of a needle valve to the existing system allows complex travel moves to be performed without stringing, greatly increasing the available part complexity of the printer. The needle valve is actuated by one of the 3D printer’s extruder stepper motors and can be controlled by g-code generated by off-the shelf slicer software. This paper also proposes a mathematical model for predicting the oozing volume of the valve, which is the small quantity of material that escapes through the nozzle during valve actuation. This model is important for calibration and g-code generation, which could be integrated with commercial slicers to use this needle valve design without writing custom code. The model is tested experimentally, and results show a strong correlation between predicted and measured oozing volumes. The performance of the valve is also characterized in terms of flow rate versus needle position. Experiments showed that the needle stroke (the vertical distance that the needle travels between open and closed states of the needle valve) is the most important parameter in minimizing oozing. An optimized stroke of 0.4 mm was used, with a maximum flow rate of 346 mm3/min when using a nozzle inner bore diameter of 0.8 mm. Using the optimized valve parameters, parallel cube structures were printed to demonstrate the reduction of stringing, along with various demonstration parts including a 3Dbenchy benchmark print and a soft robotic actuator. Overall, this study demonstrates that needle valves present a reliable and low-cost approach for controlling the flow of polymer in FGF systems with a remotely connected screw extruder.

Structured Method to Reconfigure the Fatigue of Mechanical Products such as HKS in a Refrigerator

To determine the fatigue lifetime of a system such as a car, refrigerator, etc., parametric Accelerated Life Testing (ALT) can be used as an arranged way to find out its reliability and failure mechanisms a when it is subjected to continual loads The process includes: (1) parametric ALT used to establish the BX life, (2) load evaluation for designing an accelerated life test(s), (3) a tailored representative of ALTs with design adjustments, and (4) an appraisal of whether system gets to the goal for the BX lifetime. A generalized life-stress failure approach with an accelerated factor, and sample size formulation are proposed. An organized reliability approach, such as parametric ALT, can help the designer to find product defects having an effect on reliability as computed by the improvement in life, LB, and the decreasing in failure rate, λ in the design stage. As a consequence, manufacturers can stop recalls from failures of their products when introduced into the marketplace. A hinge kit system (HKS) in a domestic refrigerator that needed to be redesigned was provided as an example. After a tailored set of parameter ALTs, the HKS with design changes were expected to realize the life aim – B1 life ten years.

A novel small-animal locomotor activity recording device for biological clock research.

The rodent running-wheel recording apparatus is a reliable approach for studying circadian rhythm. This study demonstrated how to construct a simple and intelligent running-wheel recording system. The running wheel was attached to the cage's base, whereas the Hall sensor was attached to the cage's cover. Then, the RJ25 adaptor relayed the running signal to the main control board. Finally, the main control board was connected to the USB port of the computer with the USB connection. Data were collected using the online-accessible, self-created software Magturning. Through Magturning, generated data were saved and exported in real time. Afterward, the device was validated by collecting data on the locomotor activities of mice under different light conditions. In conclusion, this new device can record circadian activity of rodents. Our device is appropriate for interdisciplinary investigations related to biological clock research.

Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics.

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS. The variance of data-independent acquisition (SWATH-MS) data was isolated from sample preparation error by modelling the variance as a function of peptide signal intensity. Our SDS-assisted cartridge workflow yield a coefficient of variance (CV) of 13%-14%. By comparison, conventional (detergent-free) in-solution digestion increased the CV to 50%; in-gel digestion provided lower CVs between 14% and 20%. By filtering peptides predicting to display lower precision, we further enhance the validity of data in global comparative proteomics. These results demonstrate the detergent-based precipitation workflow is a reliable approach for in depth, label-free quantitative proteome analysis.

Comparative analysis of machine learning techniques for cryptocurrency price prediction

The emergence of cryptocurrencies has revolutionized the concept of digital currencies and attracted significant attention from financial markets. Predicting the price dynamics of cryptocurrencies is crucial but challenging due to their highly volatile and non-linear nature. This study compares the performance of various models in predicting cryptocurrency prices using three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models analyzed include Moving Average (MA), Logistic Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The objective is to uncover underlying patterns in cryptocurrency price movements and identify the most accurate and reliable approach for predicting future prices. Through the analysis, it could be observed that MA, LR, and ARIMA models struggle to capture the actual trend accurately. In contrast, LSTM and CNN-LSTM models demonstrate strong fit to the actual price trend, with CNN-LSTM exhibiting a higher level of granularity in its predictions. Results suggest that deep learning architectures, particularly CNN-LSTM, show promise in capturing the complex dynamics of cryptocurrency prices. These findings contribute to the development of improved methodologies for cryptocurrency price prediction.

Enhanced Molecularly Imprinted Fluorescent Test Strip for Rapid and Visual Detection of Norfloxacin via a Smartphone.

Paper-based test strips with on-site visual detection have become a hot spot in the field of target detection. Yet, low specific surface area and uneven deposition limit the further application of test strips. Herein, a novel "turn-on" ratio of molecularly imprinted membranes (Eu@CDs-MIMs) was successfully prepared based on a Eu complex-doped polyvinylidene fluoride membrane for the selective, rapid and on-site visual detection of norfloxacin (NOR). The formation of surface-imprinted polymer-containing carbon dots (CDs) improves the roughness and hydrophilicity of Eu@CDs-MIMs. Fluorescence lifetimes and UV absorption spectra verified that the fluorescence enhancement of CDs is based on the synergistic effect of charge transfer and hydrogen bonding between CDs and NOR. The fluorescent test strip showed a linear fluorescent response within the concentration range of 5-50 nM with a limit of detection of 1.35 nM and a short response time of 1 min. In comparison with filter paper-based test strips, Eu@CDs-MIMs exhibit a brighter and more uniform fluorescent color change from red to blue that is visible to the naked eye. Additionally, the applied ratio fluorescent test strip was combined with a smartphone to translate RGB values into concentrations for the visual and quantitative detection of NOR and verified the detection results using high-performance liquid chromatography. The portable fluorescent test strip provides a reliable approach for the rapid, visual, and on-site detection of NOR and quinolones.

Bivariate reliability analysis for floating wind turbines

Abstract Wind turbines are designed to withstand extreme wind- and wave-induced loads, hence a reliability study is vital. This study presents a bivariate reliability approach, suitable for accurate assessment of critical forces and moments, occurring within the wind turbine’s critical mechanical parts, such as the drivetrain. A ecently developed bivariate modified Weibull method has been utilized in this study. Multivariate statistical analysis is more appropriate than a univariate one, as it accounts for cross-correlations between different system components. This study employed a bivariate modified Weibull method to estimate extreme operational loads acting on a 10-mega watt (MW) semi-submersible type floating wind turbine (FWT). Longitudinal, bending, twisting, and cyclic loads being among typical load types that FWTs and associated parts are susceptible to. Furthermore, environmental loads acting on an operating FWT being impacted by incoming wind’s stochastic behavior in terms of wind speed, direction, shear, vorticity, necessitates accurate nonlinear extreme load analysis for FWT critical parts such as the drivetrain. Appropriate numerical methods were used in this study to model dynamic, structural, aerodynamic, and control aspects of the FWT system. Bending moments acting on the FWT drivetrain have been obtained from SIMPACK (Multibody Simulation Method), given realistic in-situ environmental conditions. For a 5-year return period of interest, a bivariate modified Weibull method offered robust assessment of FWT’s coupled drivetrain’s bending moments.

An accurate estimation of hand gestures using optimal modified convolutional neural network

Sign language is a non-verbal communication between deaf and dumb communities that helps them to communicate or interact with other individuals. Although many sign language recognition models restrict the gap between the deaf-dumb community and normal people, there is a growing need for effective and reliable approaches to recognizing hand gestures under complex background conditions. To attain this objective, a novel modified deep convolutional neural network-based hybrid arithmetic hunger games (MDCNN-HAHG) is developed for the accurate estimation of hand gestures. The images of hand gestures are taken from two different datasets namely First-Person Hand Action (FHPA) and Dynamic Hand Gesture (DHG) dataset are utilized as input. The data distortions present in the datasets are eliminated during the preprocessing task. After, the significant features that help to accurately recognize the kind of posture are extracted in the feature extraction process. Finally, the proposed MDCNN-HAHG model efficiently recognizes and outputs the hand gestures. To improve the recognition performance of MDCNN model, its hyperparameters such as learning rate, dropout rate, size of the convolution kernel, and pooling size are adaptively tuned and reweighted using the hybrid arithmetic hunger games (HAHG) algorithm. Accuracy, precision, recall, specificity, and F-measure performance metrics are employed to estimate the performances of the MDCNN-HAHG model. The analytic result shows that the MDCNN-HAHG technique attains greater accuracy of about 97.2% for the DHG-14 dataset, 96.13% for the DHG-28 dataset, and 92.78% for the FHPA dataset in recognizing different hand gestures.

Numerical evaluation of grain boundary electron scattering in molybdenum thin films: A critical analysis for advanced interconnects

We present a methodology for numerically evaluating grain boundary scattering in polycrystalline molybdenum (Mo) thin films. Two types of Mo films (an epitaxially grown (110)-single-crystal film and a <110>-fiber-textured polycrystalline thin film) were sputter-deposited under identical conditions on a lattice-matched (112‾0)-Al2O3 substrate and an amorphous glass substrate, respectively. This indicates equal background scattering (phonon and impurity scattering) for the two types. Further, the selected film thickness (236 nm) significantly exceeds Mo's inherent electron mean free path (11.2 nm), rendering surface scattering contributions negligible. The resistivity difference is therefore attributed to the grain boundary scattering. Using the Mayadas–Shatzkes model and an average grain size of 80.9 ± 2.8 nm (measured from a statistically significant number of grains (747)), the grain-boundary reflection coefficient for Mo films was determined to be 0.48 nm. The presented methodology, not limited to Mo, is expected to serve as a reliable analytical approach for advanced interconnects.

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

Catalyzing Battery Materials Research via Lab‐Made, Sub‐Ampere‐Hour‐Scale Pouch Cells, and Long‐Term Electrochemical Monitoring by a Reparable Reference Electrode

AbstractBattery materials research is of crucial importance to the development of next‐generation batteries. However, the transition from lab‐scale studies, typically in gram quantities, to industrially relevant ones (i.e., kilogram scale) has been holding back by challenges in scale‐up synthesis and a lack of reliable approaches to verify the electrochemical performance of the lab‐made materials. Here the design and assembly procedures of sub‐Ah‐scale pouch cells that provide validations of several lab‐made Li‐ion and Na‐ion cathode materials available in a limited quantity (&lt;5 g) are reported. These lab‐made pouch cells show superior cycle stability and consistency over the widely used coin cells, exemplified by multiple Ni‐rich layered oxide‐graphite full batteries retaining 84.29 ± 0.16% of the initial capacities over 1000 cycles. A four‐electrode pouch cell with a reparable reference electrode is further designed to monitor impedance growth and Li plating during long‐term electrochemical tests. It can be further integrated with in situ ultrasonic imaging to enable multi‐modal studies. This work provides a powerful platform to evaluate and boost the technology readiness levels of laboratory‐discovered battery materials.

Peelable Microwave Absorption Coating with Reusable and Anticorrosion Merits.

The peelable microwave absorption (MA) coating with reversible adhesion for stable presence on substrates and easy release without any residuals is highly desired in temporary electromagnetic protection, which can quickly enter and disengage the electromagnetic protection state according to the real-time changeable harsh surroundings. On the contrary, with the incorporation of abundant absorbent to achieve excellent MA ability, the tunable adhesion and sufficient cohesion are extremely challenging to fulfill the above requirement. The reported peelable coatings still have problems in controlling adhesion/cohesion strength and coating release, facing substantial residuals after peeling even using complex chemical modification or abundant additives. Herein, a peelable MA coating based on the block characteristics of polar and nonpolar segments of poly(styrene-(ethylene-co-butylene)-styrene) (SEBS) is successfully developed. The polyaniline-decorated carbon nanotube as a microwave absorber plays a positive influence on the adhesion/cohesion of the coating due to bonding interaction. The competitive effective absorption bandwidth (EAB) of 8.8 GHz and controllable yet reversible adhesion release on various substrates and complex surfaces have been achieved. The reusability endows peelable MA coating with 93% retention of EAB even after ten coating-peeling cycles. The coating with excellent chemical and adhesion stability can effectively protect substrates from salt/acid/alkali corrosion, showing over 98% retention of EAB even after 8 h of accelerated corrosion. Our peelable MA coating via a general yet reliable approach provides a prospect for temporary electromagnetic protection.

What Are We Eating? Surveying the Presence of Toxic Molecules in the Food Supply Chain Using Chromatographic Approaches.

Consumers in developed and Western European countries are becoming more aware of the impact of food on their health, and they demand clear, transparent, and reliable information from the food industry about the products they consume. They recognise that food safety risks are often due to the unexpected presence of contaminants throughout the food supply chain. Among these, mycotoxins produced by food-infecting fungi, endogenous toxins from certain plants and organisms, pesticides, and other drugs used excessively during farming and food production, which lead to their contamination and accumulation in foodstuffs, are the main causes of concern. In this context, the goals of this review are to provide a comprehensive overview of the presence of toxic molecules reported in foodstuffs since 2020 through the Rapid Alert System for Food and Feed (RASFF) portal and use chromatography to address this challenge. Overall, natural toxins, environmental pollutants, and food-processing contaminants are the most frequently reported toxic molecules, and liquid chromatography and gas chromatography are the most reliable approaches for their control. However, faster, simpler, and more powerful analytical procedures are necessary to cope with the growing pressures on the food chain supply.

P798 Study of the concordance between the enzymatic activity of Thiopurine S-methyltransferase and polymorphisms in the TPMT gene in patients with Inflammatory Bowel Disease

Abstract Background Thiopurine therapy is associated with an increased risk of serious adverse events in patients with Inflammatory Bowel Disease (IBD). Thiopurine S-methyltransferase (TPMT) is the enzyme responsible for metabolism, and its determination helps identify patients at risk of toxicity. This study aims to explore the level of correspondence between the TPMT genotype and enzymatic activity (phenotype) with the objective of preventing adverse events prior to the commencement of thiopurine therapy. Methods Consecutive patients with IBD, Crohn’s Disease (CD) or Ulcerative Colitis (UC), were included. Genotype was conducted for the most prevalent TPMT variants (TPMT*2, TPMT*3B, TPMT*3C), TPMT enzymatic activity assessment, and quantification of 6-thioguanine nucleotide (6-TGN) levels. Adverse effects were documented, along with the administration of biologics, mesalazine, ACE inhibitors or allopurinol. The primary objective was to evaluate the concordance between TPMT genotype and phenotype. Secondary objectives included determining the prevalence of genotype and phenotype within our patient cohort, 6-TGN serum levels, the prevalence of adverse events and identifying potential pharmacological inducers of the phenotype. Results A total of 218 patients were included (131 with CD and 81 with UC). Thirteen patients did not initiate thiopurines, and 34.63% (71/205) had discontinued treatment. The most observed adverse events were myelotoxicity (15.12%), gastrointestinal toxicity (12.20%), and hepatic toxicity (5.37%). TPMT activity exhibited a trimodal distribution, with 87.61% (191/218) having normal activity, 11.47% having intermediate activity, and 1% having low activity. Genotypically, 90.83% (198/218) had a wild-type genotype, 8.72% (18/218) had a heterozygous genotype, and 0.5% (1/218) had a homozygous genotype. The concordance index between phenotype and genotype was calculated at 0.57. Significantly, myelotoxicity was associated with the presence of genotypic variants (p=0.004). 6-TGN levels did not correlate with TPMT activity and concurrent medications did not affect TPMT activity . Conclusion The proactive assessment of TPMT (phenotype or genotype) before initiating thiopurine therapy effectively mitigates the occurrence of myelotoxicity in IBD patients. Importantly, the level of concordance between TPMT activity and genotype is moderate, with genotyping serving as the more reliable approach for preselecting patients susceptible to myelotoxic reactions.

A Validated Assay to Quantify Osimertinib and Its Metabolites, AZ5104 and AZ7550, from Microsampled Dried Blood Spots and Plasma.

Osimertinib is an oral small-molecule tyrosine kinase receptor inhibitor used to treat non-small cell lung cancer (NSCLC) with a sensitizing epidermal growth factor receptor mutation. Patients may experience drug toxicity and require dose deescalation. The study aimed to quantitate osimertinib and its 2 active metabolites, AZ5104 and AZ7550, in microsampled dried blood spots (DBS) collected from patients with NSCLC using a hemaPEN device and compare them with plasma drug levels. A 6-min ultrahigh-performance liquid chromatography-tandem mass spectrometry method was developed and validated using plasma and DBS. The accuracy, selectivity, matrix effect, recovery, and stability were assessed using bioanalytical validation criteria. The hematocrit effect was investigated in DBS. Drug levels were measured in 15 patients with NSCLC, and the Bland-Altman method was used to compare measurements between plasma and DBS. The validated assay determined accurate and precise quantities, respectively, for osimertinib in both plasma (93.2%-99.3%; 0.2%-2.3%) and DBS (96.7%-99.6%; 0.5%-10.3%) over a concentration of 1-729 ng/mL. The osimertinib metabolites, AZ5104 and AZ7550, were similarly validated in accordance with bioanalytical guidelines. For 30%-60% patient hematocrit, no hematocrit bias was observed with DBS for all analytes. The Bland-Altman method showed high concordance between plasma and DBS analyte levels. Stability experiments revealed that osimertinib and its metabolites were poorly stable in plasma at room temperature, whereas all analytes were stable in DBS for 10 days at room temperature. The measurement of osimertinib, AZ5104, and AZ7550 from hemaPEN microsampled DBS is a convenient and reliable approach for therapeutic drug monitoring that produces measurements consistent with plasma drug levels.

An Ultra-High-Performance Liquid Chromatography Coupled with Tandem Mass Spectrometry Method with Online Solid-Phase Extraction Sample Preparation for the High-Throughput and Sensitive Determination of Ostarine in Human Urine.

Ostarine is frequently misused as a selective androgen receptor modulator (SARM) in sports. Consequently, there is a pressing need for reliable and simple approaches to monitor its presence in biological systems. In this work, we developed a two-dimensional analytical method utilizing online solid-phase extraction (online-SPE) in conjunction with ultra-high-performance liquid chromatography and tandem mass spectrometry (triple quadrupole). This automated 2D separation approach is characterized by minimum manual steps in the sample preparation (only dilute-and-shoot), reflecting high sample throughput and the reliability of analytical data. It provides favorable performance parameters, including a limit of detection of 0.5 pg/mL, high accuracy (relative error = 1.6-7.5%), precision (relative standard deviation = 0.8-4.5%), and sensitivity. Additionally, it demonstrates excellent linearity (r2 = 0.9999) in the calibration range of 0.05 to 25 ng/mL and robustness, with no carryover effects observed. This comparative study revealed a two-decadic-order-lower LOD of the SPE-UHPLC-MS/MS method to the corresponding UHPLC-MS/MS method and the lowest one in the group of currently published LC-MS methods. The World Anti-Doping Agency screening and confirmation criteria were met through the analysis of spiked urine samples from ten healthy volunteers. Accordingly, the proposed method is suitable for routine use in antidoping laboratories.

SERS-electrochemical dual-mode detection of microRNA on same interface assisted by exonuclease III signal transformation

Dual-mode sensing has attracted more attentions which provide more accurate and reliable approach of cancer-related biomarkers. Herein, we developed a novel SERS/electrochemical dual-mode biosensor for miRNA 21 detection based on Exo III-assisted signal transformation. Firstly, the Au NPs were deposited on electrode as SERS substrate and Mn3O4/S4(DNA signal strand) was modified on Au NPs/S5 by the DNA strands S5-S4 pairing principle as hydrogen peroxide catalyst, leading to an obviously high DPV electrical signal without Raman signal. Subsequently, the presence of miRNA 21 will activate the Mn3O4/S4 to be decomposed under exonuclease III-assisted process, then the S3′ chains modified with Raman molecular Cy3(Cy3-S3′) is continuously connected to the Au NPs/S5 by DNA stands S5-S3′ pairing principle, leading to the Raman signal response and DPV signal reduction. The biosensor shows good linear calibration curves of both SERS and electrochemical sensing modes with the detection limit of 3.98 × 10−3 nM and 6.89 × 10−5 nM, respectively. This work finds an ingenious mode for dual detection of microRNA on a same interface, which opens a new strategy for SERS and electrochemical analysis.

Effect of False Lumen Occlusion Treatment With AFX VELATM, Candy-Plug Technique for Chronic Aortic Dissection.

We sought to examine midterm results and remodeling effect of false-lumen occlusion treatment using AFX VELA in case of chronic dissection repair. From June 2019 to May 2022, we performed false lumen occlusion treatment using a modified Candy-Plug technique with AFX VELA on 8 chronic aortic dissection patients with a patent false lumen. We collected operative data, short-term clinical outcomes, mid-term clinical outcomes and imaging test results. We conducted follow-up examinations at postoperative, 6-month and 1-, 2- and 3-year intervals, including contrast-enhanced computed tomography to evaluate the diameter, false lumen thrombosis and any events. The average time from the symptom onset to the thoracic endovascular repair was 81.5 (35-155)months. The aorta showed aneurysmal dilation with an average maximum short-axis diameter of 58.9 (41-91)mm. Two cases needed emergency surgery due to rupture and impending rupture. There were no postoperative deaths. Complete thrombosis within the false lumen was achieved in 6 cases (75%), but 2 cases had incomplete thrombosis, requiring additional treatment. The mean maximum diameter showed a significant decrease at 6months, 1year and 2years postoperatively compared to preoperative measurements (P < .05). We showed the results of false lumen occlusion treatment using the AFX VELA cuff. We observed favorable clinical outcomes and remodeling effects. While the long-term durability and efficacy of this technique in aortic remodeling will need to be monitored with further observation, the use of this cuff is considered a reliable approach to false lumen occlusion treatment.

Sampling Procedures for Estimating the Infant Intake of Human Milk Leptin, Adiponectin, Insulin, Glucose, and Total Lipid.

Limited attention is given to the efficacy of protocols for the estimation of infant intake of milk components when investigating their impact on infant outcomes. We compared the actual measured intake of human milk components with estimations derived from 15 protocols to determine the most reliable approach for estimating intake of HM leptin, adiponectin, insulin, glucose, and total lipid. Twenty mothers who were 3-5 months postpartum completed a 24 h milk profile study with pre-/post-feed milk samples collection. The true infant intake (control group) based on 24 h milk intake (MI) was compared to estimated infant intakes using concentrations from five sampling protocols that were multiplied by one of true infant MI, considered mean MI (800 mL), or global mean MI (766 mL). The mean measured concentrations of six samples (three sets of pre- and post-feed samples, from morning (06:00-09:00), afternoon (13:00-16:00), and evening (19:00-22:00)) multiplied by the true infant MI, mean considered MI, and global mean MI produced the most accurate estimates of infant intake of these components. Therefore, in the absence of 24 h measurements and sampling, a sampling protocol comprising three sets of pre-/post-feed samples provides the most reliable infant intake estimates of HM leptin, adiponectin, insulin, glucose, and total lipid.