Glutamine/Glutamate Metabolism Studied with Magnetic Resonance Spectroscopic Imaging for the Characterization of Adrenal Nodules and Masses

Purpose. To assess glutamine/glutamate (Glx) and lactate (Lac) metabolism using proton magnetic resonance spectroscopic imaging (1H-MRS) in order to differentiate between adrenal gland nodules and masses (adenomas, pheochromocytomas, carcinomas, and metastases). Materials and Methods. Institutional review board approval and informed consent were obtained. A total of 130 patients (47 men) with 132 adrenal nodules/masses were prospectively assessed (54 +/- 14.8 years). A multivoxel system was used with a two-dimensional point-resolved spectroscopy/chemical-shift imaging sequence. Spectroscopic data were interpreted by visual inspection and peak amplitudes of lipids (Lip), choline (Cho), creatine (Cr), Lac, and Glx. Lac/Cr and Glx/Cr were calculated. Glx/Cr was assessed in relation to lesion size. Results. Statistically significant differences were observed in Glx/Cr results between adenomas and pheochromocytomas (P < 0.05), however, with a low positive predictive value (PPV). Glx levels were directly proportional to lesion size in carcinomas. A cutoff point of 1.44 was established for the differentiation between carcinomas larger versus smaller than 4 cm, with 75% sensitivity, 100% specificity, 100% PPV, and 80% accuracy. Lac/Cr results showed no differences across lesions. A cutoff point of -6.5 for Lac/Cr was established for carcinoma diagnosis. Conclusion. Glx levels are directly proportional to lesion size in carcinomas. A cutoff point of -6.5 Lac/Cr differentiates carcinomas from noncarcinomas.Universidade Federal de São Paulo, Dept Diagnost Imaging, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Endocrinol, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Urol, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Diagnost Imaging, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Endocrinol, BR-04024002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Urol, BR-04024002 São Paulo, BrazilWeb of Scienc

Influence of ethephon stimulation on latex physiological parameters and consequences on latex diagnosis implementation in rubber agro-industry

Latex Diagnosis (LD) is currently considered by Cirad and most of its rubber agro-industry partners as a routine physiological tool to optimise, at block level, the rubber yield production of the rubber plantations. Without using LD, a general stimulation recommendation per clone and per tapping year is generally applied at plantation scale, as a function of tapping cut position and direction, whatever the local and actual yield potential is. Even though this general recommendation is based on clonal physiological latex characteristics, such a global approach does not permit to consider the local specificities of the yield potential, as it avoids considering factors like soil heterogeneity, microclimate variations in larger estates and differential expression of diseases (leaf diseases, root diseases...). In this case, plantations are almost "blind" regarding suitability of the applied stimulation intensities, and uniform application of the same rate of stimulant in all homogenous cultural units may sometimes lead to optimised exploitation but may also lead locally to under exploitation in higher yield potential areas or to over exploitation in lower yield potential areas. Using LD permits to optimize the stimulation at local level (decrease of stimulation when an over exploitation is detected, increase of stimulation intensity when an under exploitation is detected) and therefore permits the yield optimisation block per block, taking into account the plantations heterogeneities and therefore the actual local yield potential. Of course, LD interpretation depends on former set up LD parameters reference values. These ones are clonal and established for the 4 parameters used in LD: latex sucrose content, latex inorganic phosphorus content, latex reduced thiols content and DRC/TSC. These LD reference values are established for 5 limit levels (very low, low, normal, high and very high), for each LD parameter (Suc, Pi, RSH and DRC/TSC), either at regional scale or, in case of large estates and companies, at plantation scale when local LD parameters database is large enough. To set up correctly these LD reference values, it is required to know what can be the general evolution of the 4 LD parameters depending on exploitation intensity. These evolutions are detailed in the document. (Résumé d'auteur

Writing Outside the Soviet Canon: Aleksandr Kozachinskii\u27s the Green Wagon As Roman a Clef and Odesa Memoir

This essay analyzes Aleksandr Kozachinskii’s 1938 Russian-language novella “The Green Wagon” as a roman à clef and exemplar of the Odesa Myth that has been unjustly neglected in literary scholarship. Reasons for the neglect of “The Green Wagon” include the historical context of its publication, between the Great Purges of 1936–1938 and the outbreak of World War II; Kozachinskii’s untimely death; and the conventional interpretation of the novella that reduces it to a fictionalized account of Kozachinskii’s friendship with Evgenii Petrov in Odesa during the early Soviet period. Against such a reductionist reading, and on the basis of recent archival-based scholarship on Kozachinskii’s biography, I argue that “The Green Wagon” should instead be understood as a double memoir, disguised as a roman à clef, of distinct episodes of Kozachinskii’s past as both criminal element and police investigator. The essay explores the ways in which Kozachinskii simultaneously discloses and conceals the memoiristic character of his text against the background of Stalin-era practices of self-fashioning and police-supervised confessions during the time of the Great Purges

Serum thyrotropin concentration in children with isolated thyroid nodules.

OBJECTIVE: To investigate the correlation between serum thyroid-stimulating hormone (TSH) concentration and nodule nature in pediatric patients with thyroid nodules, with the aim of identifying a marker able to differentiate benign and malignant nodules. STUDY DESIGN: This was a retrospective analysis of serum TSH concentrations in a multicentric case series of 125 pediatric patients with benign and malignant thyroid nodules. RESULTS: Of the 125 patients, 99 had benign thyroid nodules and 26 had differentiated thyroid cancer (24 papillary and 2 follicular). Final diagnosis was based on surgery in 57 cases and on a benign cytology plus clinical follow-up in 68 cases. Serum TSH concentration was significantly higher in patients with thyroid cancer compared with those with benign nodules (3.23 ± 1.59 mU/L vs 1.64 ± 0.99 mU/L; P < .001). Binary logistic regression analysis revealed that serum TSH was the sole predictor of malignancy (P < .001). Dividing the patient cohort into 5 groups based on serum TSH quintiles (TSH cutoffs 0.40, 1.00, 1.50, 1.80, and 2.80 mU/L), we observed that cancer prevalence increased in parallel with serum TSH (P < .001), with respective rates of 0%, 4%, 16%, 32%, and 52% in the 5 quintile groups. CONCLUSION: Because cases with malignant nodules are most likely seen in the upper normal serum TSH range (ie, >2.8 mU/L), serum TSH concentration can serve as a predictor of thyroid cancer in pediatric patients with thyroid nodules and can inform the decision of when to submit patients to further investigation by cytology

The fate of acetic acid during glucose co-metabolism by the spoilage yeast Zygosaccharomyces bailii

Zygosaccharomyces bailii is one of the most widely represented spoilage yeast species, being able to metabolise acetic acid in the presence of glucose. To clarify whether simultaneous utilisation of the two substrates affects growth efficiency, we examined growth in single- and mixed-substrate cultures with glucose and acetic acid. Our findings indicate that the biomass yield in the first phase of growth is the result of the weighted sum of the respective biomass yields on single-substrate medium, supporting the conclusion that biomass yield on each substrate is not affected by the presence of the other at pH 3.0 and 5.0, at least for the substrate concentrations examined. In vivo(13)C-NMR spectroscopy studies showed that the gluconeogenic pathway is not operational and that [2-(13)C]acetate is metabolised via the Krebs cycle leading to the production of glutamate labelled on C(2), C(3) and C(4). The incorporation of [U-(14)C]acetate in the cellular constituents resulted mainly in the labelling of the protein and lipid pools 51.5% and 31.5%, respectively. Overall, our data establish that glucose is metabolised primarily through the glycolytic pathway, and acetic acid is used as an additional source of acetyl-CoA both for lipid synthesis and the Krebs cycle. This study provides useful clues for the design of new strategies aimed at overcoming yeast spoilage in acidic, sugar-containing food environments. Moreover, the elucidation of the molecular basis underlying the resistance phenotype of Z. bailii to acetic acid will have a potential impact on the improvement of the performance of S. cerevisiae industrial strains often exposed to acetic acid stress conditions, such as in wine and bioethanol production.This work was supported by Fundacao para a Ciencia e Tecnologia (FCT), Portugal Grant PTDC/AGR-ALI/102608/2008 and by project FCOMP-01-0124-FEDER- 007047 and by FEDER through POFC - COMPETE and national funds from FCT - project PEst-C/BIA/UI4050/2011. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy

Counterfactual explanations are an emerging tool to enhance interpretability of deep learning models. Given a sample, these methods seek to find and display to the user similar samples across the decision boundary. In this paper, we propose a generative adversarial counterfactual approach for satellite image time series in a multi-class setting for the land cover classification task. One of the distinctive features of the proposed approach is the lack of prior assumption on the targeted class for a given counterfactual explanation. This inherent flexibility allows for the discovery of interesting information on the relationship between land cover classes. The other feature consists of encouraging the counterfactual to differ from the original sample only in a small and compact temporal segment. These time-contiguous perturbations allow for a much sparser and, thus, interpretable solution. Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy

Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling

Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this setting already poses challenges due to potential distribution shifts between domains, an even more complex scenario arises when source and target data differs in modality representation (e.g. they are acquired by sensors with different characteristics). For instance, in remote sensing, images may be collected via various acquisition modes (e.g. optical or radar), different spectral characteristics (e.g. RGB or multi-spectral) and spatial resolutions. Such a setting is denoted as Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) and it exhibits an even more severe distribution shift due to modality heterogeneity across domains.To cope with the challenging SSHDA setting, here we introduce SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) an end-to-end neural framework tailored to learning a target domain classifier by leveraging both labelled and unlabelled data from heterogeneous data sources. SHeDD is designed to effectively disentangle domain-invariant representations, relevant for the downstream task, from domain-specific information, that can hinder the cross-modality transfer. Additionally, SHeDD adopts an augmentation-based consistency regularization mechanism that takes advantages of reliable pseudo-labels on the unlabelled target samples to further boost its generalization ability on the target domain. Empirical evaluations on two remote sensing benchmarks, encompassing heterogeneous data in terms of acquisition modes and spectral/spatial resolutions, demonstrate the quality of SHeDD compared to both baseline and state-of-the-art competing approaches. Our code is publicly available here: https://github.com/tanodino/SSHDA

Cell-Free Synthesis of the Mitochondrial ADP/ATP Carrier Protein of Neurospora crassa

ADP/ATP carrier protein was synthesized in heterologous cell-free systems programmed with Neurospora poly(A)-containing RNA and homologous cell-free systems from Neurospora. The apparent molecular weight of the product obtained in vitro was the same as that of the authentic mitochondrial protein. The primary translation product obtained in reticulocyte lysates starts with formylmethionine when formylated initiator methionyl-tRNA (fMet-tRNAfMet) was present. The product synthesized in vitro was released from the ribosomes into the postribosomal supernatant. The evidence presented indicates that the ADP/ATP carrier is synthesized as a polypeptide with the same molecular weight as the mature monomeric protein and does not carry an additional sequence

Reuse out-of-year data to enhance land cover mapping via feature disentanglement and contrastive learning

Timely up-to-date land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and facilitating well-informed and sustainable decision-making. Typically, when creating a land cover (LC) map, precise ground truth data is collected through time-consuming and expensive field campaigns. This data is then utilized in conjunction with satellite image time series (SITS) through advanced machine learning algorithms to get the final map. Unfortunately, each time this process is repeated (e.g., annually over a region to estimate agricultural production or potential biodiversity loss), new ground truth data must be collected, leading to the complete disregard of previously gathered reference data despite the substantial financial and time investment they have required. How to make value of historical data, from the same or similar study sites, to enhance the current LULC mapping process constitutes a significant challenge that could enable the financial and human-resource efforts invested in previous data campaigns to be valued again. Aiming to tackle this important challenge, we here propose a deep learning framework based on recent advances in domain adaptation and generalization to combine remote sensing and reference data coming from two different domains (e.g. historical data and fresh ones) to ameliorate the current LC mapping process. Our approach, namely REFeD (data Reuse with Effective Feature Disentanglement for land cover mapping), leverages a disentanglement strategy, based on contrastive learning, where invariant and specific per-domain features are derived to recover the intrinsic information related to the downstream LC mapping task and alleviate possible distribution shifts between domains. Additionally, REFeD is equipped with an effective supervision scheme where feature disentanglement is further enforced via multiple levels of supervision at different granularities. The experimental assessment over two study areas covering extremely diverse and contrasted landscapes, namely Koumbia (located in the West-Africa region, in Burkina Faso) and Centre Val de Loire (located in centre Europe, France), underlines the quality of our framework and the obtained findings demonstrate that out-of-year information coming from the same (or similar) study site, at different periods of time, can constitute a valuable additional source of information to enhance the LC mapping process

PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers

Computer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assumptions on the geometric properties of the discovered parts; they should be small and compact. Although this prior is useful in some cases, in this paper we show that pre-trained transformer-based vision models, such as self-supervised DINOv2 ViT, enable the relaxation of these constraints. In particular, we find that a total variation (TV) prior, which allows for multiple connected components of any size, substantially outperforms previous work. We test our approach on three fine-grained classification benchmarks: CUB, PartImageNet and Oxford Flowers, and compare our results to previously published methods as well as a re-implementation of the state-of-the-art method PDiscoNet with a transformer-based backbone. We consistently obtain substantial improvements across the board, both on part discovery metrics and the downstream classification task, showing that the strong inductive biases in self-supervised ViT models require to rethink the geometric priors that can be used for unsupervised part discovery.Comment: Accepted as a main conference paper at the European Conference of Computer Vision (ECCV) 202