The molecular control of tomato fruit quality traits: the trade off between visual attributes, shelf life and nutritional value

Tomato (Solanum lycopersicum) is an established model to study fleshy fruit development and ripening and is an important crop in terms of its economic and nutritional value. Tomato fruit quality is a function of metabolite content which is prone to physiological changes related to fruit development and ripening. It has been described some ripening tomato mutants, delayed fruit deterioration (DFD), non-ripening (NOR) and ripening-inhibitor (RIN) which substantially extend “shelf life” in tomato for up to several months when defined in terms of softening, water loss and resistance to postharvest biotic infection. However, it is not known whether this extension in “shelf life” is in fact a desirable objective from the perspective of nutritional quality of the fruits. The aim of this work was to use a metabolomics approach join to genomic tools to characterize compositional changes (sugars, amino acids, organic acids and carotenoids) of non-softening tomato mutants reported (DFD, NOR and RIN) in comparison with the normally softening fruits (Ailsa Craig and M82) during ripening and postharvest shelf-life. Important results related with ripening gene expression and metabolic evolutions are shown

Ambient vibration survey of the Bosporus Suspension Bridge

This is the peer reviewed version of the article, which has been published in final form at DOI 10.1002/eqe.4290180210. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Traffic and wind excitation has been used to obtain the dynamic characteristics of the first Bosporus (Bogazici) Suspension Bridge. Structural symmetry and the absence of suspended side-spans allowed attention to be focused on the main span and the Asian tower. For the main span, 18 vertical and 20 lateral modes were obtained, including torsional modes. For the tower, 12 longitudinal and 12 lateral modes were identified. All these models lie in the range 0–1-1 Hz. A detailed comparison is given between these modes and corresponding calculated modes, obtained by use of a three-dimensional finite element model which includes a geometric stiffness matrix. Of particular interest is the validity of the theoretical model used for the box-deck, because of its subsequent use in response studies of asynchronous seismic excitation. Comparison with a more limited study made in 1973 shows that the bridge continues to behave as it was designed to behave, particularly with regard to the deck-tower interface. From natural frequency measurements of two hangers, the load which they carry was assessed

Numerical model of light propagation through Fabry-Perot etalons composed of interfaces with non-planar surface topography

We present a model that calculates optical fields reflected and transmitted by a Fabry-Perot (FP) etalon composed of interfaces with non-planar surface topography. The model uses the Rayleigh-Rice theory, which predicts the fields reflected and transmitted by a single interface, to account for the non-planar surface topography of each interface. The Rayleigh-Rice theory is evaluated iteratively to account for all round trips that light can take within the FP etalon. The model predictions can then be used to compute Interferometer transfer function (ITF)s, by performing wavelength or angle resolved simulations enabling predictions of the bandwidth, peak transmissivity, and sensitivity of FP etalons. The model was validated against the Pseudospectral time-domain (PSTD) method, which resulted in good agreement. Since the model accuracy is expected to reduce as the Root mean square (RMS) of the topographic map increases, the error in the model’s predictions was studied as a function of topographic map RMS. Finally, application of the model was exemplified by predicting the impact of roughness on ITFs and computing the changes in FP etalon transmissivity as cavity thickness is modulated by an ultrasonic wave

Assessment of myocardial injury after reperfused infarction by T1ρ cardiovascular magnetic resonance.

BackgroundThe evolution of T1ρ and of other endogenous contrast methods (T2, T1) in the first month after reperfused myocardial infarction (MI) is uncertain. We conducted a study of reperfused MI in pigs to serially monitor T1ρ, T2 and T1 relaxation, scar size and transmurality at 1 and 4 weeks post-MI.MethodsTen Yorkshire swine underwent 90 min of occlusion of the circumflex artery and reperfusion. T1ρ, T2 and native T1 maps and late gadolinium enhanced (LGE) cardiovascular magnetic resonance (CMR) data were collected at 1 week (n = 10) and 4 weeks (n = 5). Semi-automatic FWHM (full width half maximum) thresholding was used to assess scar size and transmurality and compared to histology. Relaxation times and contrast-to-noise ratio were compared in healthy and remote myocardium at 1 and 4 weeks. Linear regression and Bland-Altman was performed to compare infarct size and transmurality.ResultsRelaxation time differences between infarcted and remote myocardial tissue were ∆T1 (infarct-remote) = 421.3 ± 108.8 (1 week) and 480.0 ± 33.2 ms (4 week), ∆T1ρ = 68.1 ± 11.6 and 74.3 ± 14.2, and ∆T2 = 51.0 ± 10.1 and 59.2 ± 11.4 ms. Contrast-to-noise ratio was CNRT1 = 7.0 ± 3.5 (1 week) and 6.9 ± 2.4 (4 week), CNRT1ρ = 12.0 ± 6.2 and 12.3 ± 3.2, and CNRT2 = 8.0 ± 3.6 and 10.3 ± 5.8. Infarct size was not significantly different for T1ρ, T1 and T2 compared to LGE (p = 0.14) and significantly decreased from 1 to 4 weeks (p < 0.01). Individual infarct size changes were ∆T1ρ = -3.8%, ∆T1 = -3.5% and ∆LGE = -2.8% from 1 - 4 weeks, but there was no observed change in infarct size for T2 or histologically.ConclusionsT1ρ was highly correlated with alterations left ventricle (LV) pathology at 1 and 4 weeks post-MI and therefore it may be a useful method endogenous contrast imaging of infarction

Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction

In this research, we developed a graph-based framework to represent various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 30% of the labeled data to predict the systems' performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 70% of the data, which serves as the test set. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via the open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios.Comment: 13 pages, 17 figure

Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems

As mechanical systems become more complex and technological advances accelerate, the traditional reliance on heritage designs for engineering endeavors is being diminished in its effectiveness. Considering the dynamic nature of the design industry where new challenges are continually emerging, alternative sources of knowledge need to be sought to guide future design efforts. One promising avenue lies in the analysis of design optimization data, which has the potential to offer valuable insights and overcome the limitations of heritage designs. This paper presents a step toward extracting knowledge from optimization data in multi-split fluid-based thermal management systems using different classification machine learning methods, so that designers can use it to guide decisions in future design efforts. This approach offers several advantages over traditional design heritage methods, including applicability in cases where there is no design heritage and the ability to derive optimal designs. We showcase our framework through four case studies with varying levels of complexity. These studies demonstrate its effectiveness in enhancing the design of complex thermal management systems. Our results show that the knowledge extracted from the configuration design optimization data provides a good basis for more general design of complex thermal management systems. It is shown that the objective value of the estimated optimal configuration closely approximates the true optimal configuration with less than 1 percent error, achieved using basic features based on the system heat loads without involving the corresponding optimal open loop control (OLOC) features. This eliminates the need to solve the OLOC problem, leading to reduced computation costs.Comment: 13 pages, 20 figure

Multi-split configuration design for fluid-based thermal management systems

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow-split are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The architectures discussed here can be used for various applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include 3 categories of cases and are discussed in detail.Comment: 11 pages, 18 figure

Vitamin D Status and its Association with Morbidity including Wasting and Opportunistic Illnesses in HIV-Infected Women in Tanzania.

Vitamin D has a potential role in preventing HIV-related complications, based on its extensive involvement in immune and metabolic function, including preventing osteoporosis and premature cardiovascular disease. However, this association has not been examined in large studies or in resource-limited settings. Vitamin D levels were assessed in 884 HIV-infected pregnant women at enrollment in a trial of multivitamin supplementation (excluding vitamin D) in Tanzania. Information on HIV related complications was recorded during follow-up (median, 70 months). Proportional hazards models and generalized estimating equations were used to assess the relationship of vitamin D status with these outcomes. Women with low vitamin D status (serum 25-hydroxyvitamin D<32 ng/mL) had 43% higher risk of reaching a body mass index (BMI) less than 18 kg/m(2) during the first 2 years of follow-up, compared to women with adequate vitamin D levels (hazard ratio [HR]: 1.43; 95% confidence intervals: [1.03-1.99]). The relationship between continuous vitamin D levels and risk of BMI less than 18 kg/m(2) during follow-up was inverse and linear (p=0.03). Women with low vitamin D levels had significantly higher incidence of acute upper respiratory infections (HR: 1.27 [1.04-1.54]) and thrush (HR: 2.74 [1.29-5.83]) diagnosed during the first 2 years of follow-up. Low vitamin D status was a significant risk factor for wasting and HIV-related complications such as thrush during follow-up in this prospective cohort in Tanzania. If these protective associations are confirmed in randomized trials, vitamin D supplementation could represent a simple and inexpensive method to improve health and quality of life of HIV-infected patients, particularly in resource-limited settings

Exploring the roles of urinary HAI-1, EpCAM and EGFR in bladder cancer prognosis and risk stratification

Objectives: To investigate whether elevated urinary HAI-1, EpCAM and EGFR are independent prognostic biomarkers within non-muscle-invasive bladder cancer (NMIBC) patients, and have utility for risk stratification to facilitate treatment decisions. Results: After accounting for EAU risk group in NMIBC patients, the risk of BC-specific death was 2.14 times higher (95% CI: 1.08 to 4.24) if HAI-1 was elevated and 2.04 times higher (95% CI: 1.02 to 4.07) if EpCAM was elevated. The majority of events occurred in the high-risk NMIBC group and this is where the biggest difference is seen in the survival curves when plotted for EAU risk groups separately. In MIBC patients, being elevated for any of the three biomarkers was significantly associated with BC-specific mortality after accounting for other risk factors, HR = 4.30 (95% CI: 1.85 to 10.03). Patients and Methods: Urinary levels of HAI-1, EpCAM and EGFR were measured by ELISA in 683 and 175 patients with newly-diagnosed NMIBC and MIBC, respectively, recruited to the Bladder Cancer Prognosis Programme. Associations between biomarkers and progression, BC-specific mortality and all-cause mortality were evaluated using univariable and multivariable Cox regression models, adjusted for European Association of Urology (EAU) NMIBC risk groups. The upper 25% of values for each biomarker within NMIBC patients were considered as elevated. Exploratory analyses in urine from MIBC patients were also undertaken. Conclusion: Urinary HAI-1 and EpCAM are prognostic biomarkers for NMIBC patients. These biomarkers have potential to guide treatment decisions for high-risk NMIBC patients. Further analyses are required to define the roles of HAI-1, EpCAM and EGFR in MIBC patients