Classification of the Single Oleosin Isoform and Characterization of Seed Oil Bodies in Gymnosperms

Hemocytes, the cells present in the hemolymph of insects and other invertebrates, perform several physiological functions, including innate immunity. The current classification of hemocyte types is based mostly on morphological features; however, divergences have emerged among specialists in triatomines, the insect vectors of Chagas’ disease (Hemiptera: Reduviidae). Here, we have combined technical approaches in order to characterize the hemocytes from fifth instar nymphs of the triatomine Dipetalogaster maxima. Moreover, in this work we describe, for the first time, the ultrastructural features of D. maxima hemocytes. Using phase contrast microscopy of fresh preparations, five hemocyte populations were identified and further characterized by immunofluorescence, flow cytometry and transmission electron microscopy. The plasmatocytes and the granulocytes were the most abundant cell types, although prohemocytes, adipohemocytes and oenocytes were also found. This work sheds light on a controversial aspect of triatomine cell biology and physiology setting the basis for future in-depth studies directed to address hemocyte classification using non-microscopy-based markers.

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Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽

10.1007/s10346-021-01654-0 ◽

2021 ◽

Author(s):

Chiara Crippa ◽

Elena Valbuzzi ◽

Paolo Frattini ◽

Giovanni B. Crosta ◽

Margherita C. Spreafico ◽

...

Keyword(s):

Rock Slope ◽

Progressive Failure ◽

Principal Component ◽

Cost Effective ◽

Displacement Rate ◽

Machine Learning Classification ◽

Management Perspective ◽

Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

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The Underlying Order Induced by Orthogonality and the Quantum Speed Limit

Quantum Reports ◽

10.3390/quantum3030024 ◽

2021 ◽

Vol 3 (3) ◽

pp. 376-388

Author(s):

Francisco J. Sevilla ◽

Andrea Valdés-Hernández ◽

Alan J. Barrios

Keyword(s):

Energy Spectrum ◽

Energy Distribution ◽

Finite Time ◽

Complete Characterization ◽

Comprehensive Analysis ◽

Orthogonality Condition ◽

Speed Limit ◽

Physical Quantities

We perform a comprehensive analysis of the set of parameters {ri} that provide the energy distribution of pure qutrits that evolve towards a distinguishable state at a finite time τ, when evolving under an arbitrary and time-independent Hamiltonian. The orthogonality condition is exactly solved, revealing a non-trivial interrelation between τ and the energy spectrum and allowing the classification of {ri} into families organized in a 2-simplex, δ2. Furthermore, the states determined by {ri} are likewise analyzed according to their quantum-speed limit. Namely, we construct a map that distinguishes those ris in δ2 correspondent to states whose orthogonality time is limited by the Mandelstam–Tamm bound from those restricted by the Margolus–Levitin one. Our results offer a complete characterization of the physical quantities that become relevant in both the preparation and study of the dynamics of three-level states evolving towards orthogonality.

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Molecular Classification and Tumor Microenvironment Characterization of Gallbladder Cancer by Comprehensive Genomic and Transcriptomic Analysis

Cancers ◽

10.3390/cancers13040733 ◽

2021 ◽

Vol 13 (4) ◽

pp. 733

Author(s):

Nobutaka Ebata ◽

Masashi Fujita ◽

Shota Sasagawa ◽

Kazuhiro Maejima ◽

Yuki Okawa ◽

...

Keyword(s):

Tumor Microenvironment ◽

Gallbladder Cancer ◽

Epithelial To Mesenchymal Transition ◽

Molecular Classification ◽

Mesenchymal Transition ◽

Elevated Expression ◽

Fresh Frozen ◽

Therapeutic Decision Making

Gallbladder cancer (GBC), a rare but lethal disease, is often diagnosed at advanced stages. So far, molecular characterization of GBC is insufficient, and a comprehensive molecular portrait is warranted to uncover new targets and classify GBC. We performed a transcriptome analysis of both coding and non-coding RNAs from 36 GBC fresh-frozen samples. The results were integrated with those of comprehensive mutation profiling based on whole-genome or exome sequencing. The clustering analysis of RNA-seq data facilitated the classification of GBCs into two subclasses, characterized by high or low expression levels of TME (tumor microenvironment) genes. A correlation was observed between gene expression and pathological immunostaining. TME-rich tumors showed significantly poor prognosis and higher recurrence rate than TME-poor tumors. TME-rich tumors showed overexpression of genes involved in epithelial-to-mesenchymal transition (EMT) and inflammation or immune suppression, which was validated by immunostaining. One non-coding RNA, miR125B1, exhibited elevated expression in stroma-rich tumors, and miR125B1 knockout in GBC cell lines decreased its invasion ability and altered the EMT pathway. Mutation profiles revealed TP53 (47%) as the most commonly mutated gene, followed by ELF3 (13%) and ARID1A (11%). Mutations of ARID1A, ERBB3, and the genes related to the TGF-β signaling pathway were enriched in TME-rich tumors. This comprehensive analysis demonstrated that TME, EMT, and TGF-β pathway alterations are the main drivers of GBC and provides a new classification of GBCs that may be useful for therapeutic decision-making.

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Characterization of Unripe and Mature Avocado Seed Oil in Different Proportions as Phase Change Materials and Simulation of Their Cooling Storage

Molecules ◽

10.3390/molecules26010107 ◽

2020 ◽

Vol 26 (1) ◽

pp. 107

Author(s):

Evelyn Reyes-Cueva ◽

Juan Francisco Nicolalde ◽

Javier Martínez-Gómez

Keyword(s):

Phase Change ◽

Phase Change Materials ◽

Seed Oil ◽

Energy System ◽

Maturity Stage ◽

Natural Environments ◽

Renewable Materials ◽

Scanning Calorimetry ◽

Avocado Seed

Environmental problems have been associated with energy consumption and waste management. A solution is the development of renewable materials such as organic phase change materials. Characterization of new materials allows knowing their applications and simulations provide an idea of how they can developed. Consequently, this research is focused on the thermal and chemical characterization of five different avocado seed oils depending on the maturity stage of the seed: 100% unripe, 25% mature-75% unripe, 50% mature-50% unripe, 75% mature-25% unripe, and 100% mature. The characterization was performed by differential scanning calorimetry, Fourier transform infrared spectroscopy, and thermogravimetric analysis. The best oil for natural environments corresponded to 100% matured seed with an enthalpy of fusion of 52.93 J·g−1, and a degradation temperature between 241–545 °C. In addition, the FTIR analysis shows that unripe seed oil seems to contain more lipids than a mature one. Furthermore, a simulation with an isothermal box was conducted with the characterized oil with an initial temperature of −14 °C for the isothermal box, −27 °C for the PCM box, and an ambient temperature of 25 °C. The results show that without the PCM the temperature can reach −8 °C and with it is −12 °C after 7 h, proving its application as a cold thermal energy system.

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Practical classification of triple-negative breast cancer: intratumoral heterogeneity, mechanisms of drug resistance, and novel therapies

npj Breast Cancer ◽

10.1038/s41523-020-00197-2 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Antonio Marra ◽

Dario Trapani ◽

Giulia Viale ◽

Carmen Criscitiello ◽

Giuseppe Curigliano

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Residual Disease ◽

Therapeutic Approaches ◽

Minimal Residual Disease Monitoring ◽

Anticancer Treatment ◽

Immune Contexture

Abstract Triple-negative breast cancer (TNBC) is not a unique disease, encompassing multiple entities with marked histopathological, transcriptomic and genomic heterogeneity. Despite several efforts, transcriptomic and genomic classifications have remained merely theoretic and most of the patients are being treated with chemotherapy. Driver alterations in potentially targetable genes, including PIK3CA and AKT, have been identified across TNBC subtypes, prompting the implementation of biomarker-driven therapeutic approaches. However, biomarker-based treatments as well as immune checkpoint inhibitor-based immunotherapy have provided contrasting and limited results so far. Accordingly, a better characterization of the genomic and immune contexture underpinning TNBC, as well as the translation of the lessons learnt in the metastatic disease to the early setting would improve patients’ outcomes. The application of multi-omics technologies, biocomputational algorithms, assays for minimal residual disease monitoring and novel clinical trial designs are strongly warranted to pave the way toward personalized anticancer treatment for patients with TNBC.

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