Radiogenomics in clear cell renal cell carcinoma: correlations between advanced CT imaging (texture analysis) and microRNAs expression

Purpose: A relevant challenge for the improvement of clear cell renal cell carcinoma management could derive from the identification of novel molecular biomarkers that could greatly improve the diagnosis, prognosis, and treatment choice of these neoplasms. In this study, we investigate whether quantitative parameters obtained from computed tomography texture analysis may correlate with the expression of selected oncogenic microRNAs. Methods: In a retrospective single-center study, multiphasic computed tomography examination (with arterial, portal, and urographic phases) was performed on 20 patients with clear cell renal cell carcinoma and computed tomography texture analysis parameters such as entropy, kurtosis, skewness, mean, and standard deviation of pixel distribution were measured using multiple filter settings. These quantitative data were correlated with the expression of selected microRNAs (miR-21-5p, miR-210-3p, miR-185-5p, miR-221-3p, miR-145-5p). Both the evaluations (microRNAs and computed tomography texture analysis) were performed on matched tumor and normal corticomedullar tissues of the same patients cohort. Results: In this pilot study, we evidenced that computed tomography texture analysis has robust parameters (eg, entropy, mean, standard deviation) to distinguish normal from pathological tissues. Moreover, a higher coefficient of determination between entropy and miR-21-5p expression was evidenced in tumor versus normal tissue. Interestingly, entropy and miR-21-5p show promising correlation in clear cell renal cell carcinoma opening to a radiogenomic strategy to improve clear cell renal cell carcinoma management. Conclusion: In this pilot study, a promising correlation between microRNAs and computed tomography texture analysis has been found in clear cell renal cell carcinoma. A clear cell renal cell carcinoma can benefit from noninvasive evaluation of texture parameters in adjunction to biopsy results. In particular, a promising correlation between entropy and miR-21-5p was found

Highly efficient planar perovskite solar cells through band alignment engineering

The simplification of perovskite solar cells (PSCs), by replacing the mesoporous electron selective layer (ESL) with a planar one, is advantageous for large-scale manufacturing. PSCs with a planar TiO2 ESL have been demonstrated, but these exhibit unstabilized power conversion efficiencies (PCEs). Herein we show that planar PSCs using TiO2 are inherently limited due to conduction band misalignment and demonstrate, with a variety of characterization techniques, for the first time that SnO2 achieves a barrier-free energetic configuration, obtaining almost hysteresis-free PCEs of over 18% with record high voltages of up to 1.19 V

Urinary Bisphenol A Concentrations and Implantation Failure among Women Undergoing in Vitro Fertilization

Background: Bisphenol A (BPA) is a synthetic chemical widely used in the production of polycarbonate plastic and epoxy resins found in numerous consumer products. In experimental animals, BPA increases embryo implantation failure and reduces litter size. Objective: We evaluated the association of urinary BPA concentrations with implantation failure among women undergoing in vitro fertilization (IVF). Methods: We used online solid phase extraction–high performance liquid chromatography–isotope dilution tandem mass spectrometry to measure urinary BPA concentrations in 137 women in a prospective cohort study among women undergoing IVF at the Massachusetts General Hospital Fertility Center in Boston, Massachusetts. We used logistic regression to evaluate the association of cycle-specific urinary BPA concentrations with implantation failure, accounting for correlation among multiple IVF cycles in the same woman using generalized estimating equations. Implantation failure was defined as a negative serum β-human chorionic gonadotropin test (β-hCG < 6 IU/L) 17 days after egg retrieval. Results: Among 137 women undergoing 180 IVF cycles, urinary BPA concentrations had a geometric mean (SD) of 1.53 (2.22) µg/L. Overall, 42% (n = 75) of the IVF cycles resulted in implantation failure. In adjusted models, there was an increased odds of implantation failure with higher quartiles of urinary BPA concentrations {odds ratio (OR) 1.02 [95% confidence interval (CI): 0.35, 2.95}, 1.60 (95% CI: 0.70, 3.78), and 2.11 (95% CI: 0.84, 5.31) for quartiles 2, 3, and 4, respectively, compared with the lowest quartile (p-trend = 0.06). Conclusion: There was a positive linear dose–response association between BPA urinary concentrations and implantation failure

Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks

To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (∼10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean R2 values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the R2, other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain

Image-based sensing of salt stress in grapevine

Grapevine is among the most economically important crops suffering environmental constraints, including drought and salt stress. Although imaging is increasingly used to detect abiotic stress in agriculture, image-based phenotyping in grapevine still needs optimisation. This study presents the RGB-(red, green, blue)-based phenotyping of the early stage of salt stress response in potted grapevine (Aleatico/SO4) irrigated with saline water (100 mM NaCl) for 9 days in contrast with vines irrigated with fresh water. The response was measured using stomatal conductance (gs), net photosynthetic rate (A), transpiration (E), maximum potential photosynthetic efficiency (Fv/Fm), stem water potential (SWP) concurrently with RGB imaging via a robotised platform. The image-based phenotyping of salt-stressed vines employed two sets of measurements: (i) the pixel fraction of specific colour bands (Yellow, Green, Brown and Dark Green) and (ii) the mean pixel value of R, G and B and other RGB-based colorimetric indexes. Results show that the responses of gs, A, E, Fv/Fm were closely related to increasing soil electrical conductivity (EC) and that imaging could detect the EC threshold of approx. 4 dS m-1 causing a 60 % decrease in these physiological traits compared to the pre-stress level. The SWP declined to about -0.7 MPa at the end of the experiment. The change of the relative pixel fraction of Dark Green to increasing EC has been analysed within a dose-response context, showing that a decrease of 1 % of the Dark Green colour band corresponded to the 4 dS m-1 EC threshold. This study also examined the use of the mean pixel value of the R, G and B channels as proxies of EC along with new RGB-based indexes resulting from the rearrangement of original R, G and B mean pixel values. Results show the suitability of the mean pixel value of R and Coloration Index [(R-B)/R] to serve as predictors of EC (R2 &gt;= 0.80)

The Renaissance of fullerenes with perovskite solar cells

Fullerenes have been extensively used for more than two decades for the development of organic photovoltaics (OPV). While OPV seems to be a technology almost ready for the market, in the last few years fullerenes are attracting a big interest for the improvement they afford on the already well-performing perovskite solar cells (PSCs). Thanks to PSC integration, interest in fullerenes is rising again, opening up new exciting perspectives for photovoltaics. This review article aims at analyzing the landmark contributions that gave birth to the novel application of fullerenes in PSCs and to the technological solutions that are emerging with them

Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity

Effective identification of tomato plant traits is crucial for timely monitoring and evaluating their growth and harvest. However, conducting stress experiments on multiple tomato genotypes introduces challenges due to the nature of the data. One of these challenges arises from an imbalanced sample distribution, potentially leading to misclassification between classes and disruptions in model recognition. This paper addresses the effect of these challenges by considering the imbalanced classes of flowers, fruits, and nodes and proposing an improved detection approach through data balancing. A novel data-balancing approach is introduced in this study to overcome the issue of imbalanced data. The proposed solution involves the implementation of a YOLOv8 deep learning model, which effectively detects flowers, fruits, and nodes in tomato plants. This model significantly enhances the ability of the algorithm to detect objects of varying sizes within complex environments. To further bolster the recognition capability of the targeted classes, the proposed model integrates a Squeeze-and-Excitation (SE) block attention module into its head architecture. This module strengthens the model recognition ability by giving increased attention to the studied classes, thereby enhancing overall detection performance. The results demonstrate that the data balancing approach successfully improves the model performance in response to the data challenges. When applying the technique of pre-training the optimal weights obtained from balanced data on imbalanced data, the SE-block module showed significant improvements in outcomes

Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars

The paper focuses on the seasonal oil accumulation in traditional and super-high density (SHD) olive plantations and its modelling employing image-based linear models. For these purposes, at 7-10-day intervals, fruit samples (cultivar Arbequina, Fasola, Frantoio, Koroneiki, Leccino, Maiatica) were pictured and images segmented to extract the Red (R), Green (G), and Blue (B) mean pixel values which were re-arranged in 35 RGB-derived colorimetric indexes (CIs). After imaging, the samples were crushed and oil concentration was determined (NIR). The analysis of the correlation between oil and CIs revealed a differential hysteretic behavior depending on the covariates (CI and cultivar). The hysteresis area (Hyst) was then quantified and used to rank the CIs under the hypothesis that CIs with the maximum or minimum Hyst had the highest correlation coefficient and were the most suitable predictors within a general linear model. The results show that the predictors selected according to Hyst-based criteria had high accuracy as determined using a Global Performance Indicator (GPI) accounting for various performance metrics (R2, RSME, MAE). The use of a general linear model here presented is a new computational option integrating current methods mostly based on artificial neural networks. RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the transition of the olive sector towards a digital agriculture domain

Functionalization of transparent conductive oxide electrode for TiO2-free perovskite solar cells

Many of the best performing solar cells based on perovskite-halide light absorbers use TiO2 as an electron selective contact layer. However, TiO2 usually requires high temperature sintering, is related to electrical instabilities in perovskite solar cells, and causes cell performance degradation under full solar spectrum illumination. Here we demonstrate an alternative approach based on the modification of transparent conductive oxide electrodes with self-assembled siloxane-functionalized fullerene molecules, eliminating TiO2 or any other additional electron transporting layer. We demonstrate that these molecules spontaneously form a homogenous monolayer acting as an electron selective layer on top of the fluorine doped tin oxide (FTO) electrode, minimizing material consumption. We find that the fullerene-modified FTO is a robust, chemically inert charge selective contact for perovskite based solar cells, which can reach 15% of stabilised power conversion efficiency in a flat junction device architecture using a scalable, low temperature, and reliable process. In contrast to TiO2, devices employing a molecularly thin functionalized fullerene layer show unaffected performance after 67 h of UV light exposure

Plurality of excitons in Ruddlesden-Popper metal halides and the role of the B-site metal cation

We investigate the effect of metal cation substition on the excitonic structure and dynamics in a prototypical Ruddlesden-Popper metal halide. Through an in-depth spectroscopic and theoretical analysis, we identify the presence of multiple resonances in the optical spectra of a phenethyl ammonium tin iodide, a tin-based RPMH. Based on ab initio calculations, we assign these resonances to distinct exciton series that originate from the splitting of the conduction band due to spin-orbit coupling. While the splitting energy in the tin based system is low enough to enable the observation of the higher lying exciton in the visible-range spectrum of the material, the higher splitting energy in the lead counterpart prevents the emergence of such a feature. We elucidate the critical role played by the higher lying excitonic state in the ultrafast carrier thermalization dynamics