Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipid Signaling Requires ROS Production to Elicit Actin Cytoskeleton Remodeling during Plant Innate Immunity

In terrestrial plants a basal innate immune system, pattern-triggered immunity (PTI), has evolved to limit infection by diverse microbes. The remodeling of actin cytoskeletal arrays is now recognized as a key hallmark event during the rapid host cellular responses to pathogen attack. Several actin binding proteins have been demonstrated to fine tune the dynamics of actin filaments during this process. However, the upstream signals that stimulate actin remodeling during PTI signaling remain poorly characterized. Two second messengers, reactive oxygen species (ROS) and phosphatidic acid (PA), are elevated following pathogen perception or microbe-associated molecular pattern (MAMP) treatment, and the timing of signaling fluxes roughly correlates with actin cytoskeletal rearrangements. Here, we combined genetic analysis, chemical complementation experiments, and quantitative live-cell imaging experiments to test the role of these second messengers in actin remodeling and to order the signaling events during plant immunity. We demonstrated that PHOSPHOLIPASE Dβ (PLDβ) isoforms are necessary to elicit actin accumulation in response to flg22-associated PTI. Further, bacterial growth experiments and MAMP-induced apoplastic ROS production measurements revealed that PLDβ-generated PA acts upstream of ROS signaling to trigger actin remodeling through inhibition of CAPPING PROTEIN (CP) activity. Collectively, our results provide compelling evidence that PLDβ/PA functions upstream of RBOHD-mediated ROS production to elicit actin rearrangements during the innate immune response in Arabidopsis.
Modeling compressor blades gas turbine in a marine environment to determine of the stress and possible damages

In this paper, the static simulation carried out on the blades of the seventh stage of the compressor of a gas turbine installed in an offshore oil platform is reported, the objective of this simulation is to have a means of analysis that confirms and complements the tests visual inspection (boroscopy), as well as non-destructive tests. In specific case of this compressor, the results of boroscopy were available, as well as the scanning electron microscope (SEM), with which it was possible to measure the roughness and evaluate the degradation of the blade surfaces after 30,000 hours of service operating in Cantarell, Mexico, in a very aggressive marine environment conditions, where the entry of solid particles is highly possible. The model of seventh stage’s blade was obtained with a cloud of points. A finite element analysis was carried out with ANSYS software. Results showed wear modes were originated by a severe stinging action. Also, large craters, similar to those observed in solid particle erosion, were developed by at normal impact. The points, where peak stresses were calculated, correspond to those places in which corrosion and some irregular scratches similar to plowing action, was observed. These are the points in which failures take place. Results showed wear modes were originated by a severe stinging action.
Algoritmos de Random Forest como alerta temprana para la predicción de insolvencias en empresas constructoras = Random Forest algorithms as early warning tools for the prediction of insolvencies in construction companies

The European Union's concern with preventing companies from reaching insolvency proceedings motivated the enactment of Directive (EU) 2019/1023 of the European Parliament and of the Council, and its mandatory transposition into Member States' regulations by July 17, 2021. This Directive states that debtors must have access to early warning tools to detect situations of J. I. Sordo Sierpe, M. del Río Merino, Á. Pérez Raposo, V. Vitiello 10 Anales de Edificación, Vol. 7, No 1, 9-18 (2021). ISSN: 2444-1309 imminent insolvency. This research aims to contribute to the development of such early warning tools for a very specific sector: residential and non-residential construction. The methodology has been divided into two phases, each with its own specific objective: (1) to select the predictor variables that can best explain the model (traditional statistical techniques have been used for this purpose); and (2) to select the algorithms that provide the greatest precision for the early warning tool model from among five Random Forest algorithms. The main objective of this is to obtain warning signs sufficiently enough in advance that insolvency situations can be detected. The fundamental aim is to achieve a model without using the profit and loss accounts from the construction companies under investigation. This is so to avoid the lack of objectivity that income, and therefore accounting results, may have in this sector. Accuracy percentages of over 85% were obtained three years before insolvency occurred using only balance sheet ratios. The main value is to be able to apply the early warning tool in a simple way, using little amounts of data, especially for the debtor, who can react early enough to avoid a potentially irreversible financial situation.
Silicon photonic modulators and filters for optical interconnects

The goal of this work is to enhance the performance of and demonstrate new applications for silicon photonic modulators and filters. We demonstrate a variety of novel designs and applications of silicon photonic devices for integrated optical interconnects. First, we demonstrate a biasing scheme for travelling-wave MachZehnder modulators in which the bias voltage is applied separately from the data signal. Using this biasing scheme, there is no low frequency cutoff and there is no power consumption in the termination resistor from the bias voltage, which results in an improved modulator having a lower overall power consumption. We experimentally show, as a proof-of-concept, successful high-speed modulation, at a data rate of 28 Gb/s, of a modulator which uses this biasing scheme. Next, we present a novel modulator design in which a microring modulator is placed into each arm of a Mach-Zehnder interferometer. This design uses the sharp phase response of a microring resonator near its resonance so that the light experiences a large phase change when a voltage is applied to the p-n junction phase shifters within the microring. We use temporal coupled mode theory to simulate the timedomain response of this modulator. We then demonstrate a novel modulator design which uses a quarter-wave phase-shifted Bragg grating. The modulator, which was fabricated using 193 nm optical lithography, has open eye diagrams at a data rate of 32 Gb/s. We also show that using a 231-1 pseudorandom binary sequence pattern, the modulator has a bit error rate (BER) less than 10-12 at a data rate of 20 Gb/s and has a BER less than 10-10 at a data rate of 25 Gb/s. Finally, we demonstrate a contra-directional grating coupler-based filter on silicon in an optical adddrop multiplexer configuration and show that it can successfully add and drop a 12.5 Gb/s signal at the same wavelength without substantial signal degradation.
Grid Synchronization Enhancement of Distributed Generators Using an Adaptive Phase-Locked Loop Tuning System

This paper proposes an effective algorithm; this algorithm is updating the phase-locked loop (PLL) gains according to the value of the short circuit ratio that will decrease the weak grid effect in the PLL controllers to reduce problems related to renewable energy sources and connect to the point of common coupling (PCC). Based on system parameters estimation, the proposed algorithm enhances the system stability for the distribution networks. The standard IEEE 33-bus test system is used to investigate the proposed algorithm’s effectiveness and superiority. An extensive mathematical effort is exerted to get the system’s small-signal stability (SSS) model. The tuning of the phase-locked loop and the current control loop are choosing to dynamically change according to the system variables to ensure the same system stability margin, whatever the change in the loading conditions. The MATLAB/Simulink model is used to validate the algorithm and simulate the test system.
Comparison of the probiotic effects of freshwater and marine photosynthetic bacteria in kuruma shrimp (Marsupenaeus japonicus) culture

ABSTRACT In a field study, the probiotic effects of a freshwater purple nonsulfur photosynthetic bacterium (PNSB), Rhodobacter sphaeroides, and a marine PNSB, Rhodovulum sulfidophilum, on kuruma shrimp (Marsupenaeus japonicus) were compared by using two commercially operating shrimp ponds. Shrimp were fed with feed containing 0.01% fresh weight (106 cfu/g feed) of PNSB cells for 152 d. The marine PNSB showed a better performance than the freshwater PNSB in average body weight at harvest time, survival rate (P < 0.001 by Fisher’s exact test), and feed conversion rate. The effects of freshwater and marine PNSB on shrimp gene expression were also compared by next-generation sequencing and quantitative RT-PCR. Both freshwater and marine PNSB promoted the expression of several genes related to the innate immune system, including NADPH oxidase, superoxide dismutase, lysozyme, antimicrobial peptides, and lectins, and the promotive effect of marine PNSB was stronger than that of freshwater PNSB. The cost of PNSB was calculated to be US$ 0.003–0.005 per kg feed; thus, marine PNSB is a cost-effective and feasible probiotic for shrimp aquaculture.
An Efficient Aequorea victoria Green Fluorescent Protein for Stimulated Emission Depletion Super-Resolution Microscopy

In spite of their value as genetically encodable reporters for imaging in living systems, fluorescent proteins have been used sporadically for stimulated emission depletion (STED) super-resolution imaging, owing to their moderate photophysical resistance, which does not enable reaching resolutions as high as for synthetic dyes. By a rational approach combining steady-state and ultrafast spectroscopy with gated STED imaging in living and fixed cells, we here demonstrate that F99S/M153T/V163A GFP (c3GFP) represents an efficient genetic reporter for STED, on account of no excited state absorption at depletion wavelengths <600 nm and a long emission lifetime. This makes c3GFP a valuable alternative to more common, but less photostable, EGFP and YFP/Citrine mutants for STED imaging studies targeting the green-yellow region of the optical spectrum.
Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products

Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted multiscale atomistic modeling are detailed through several cases studies. With respect to data and data mining technologies, entropy and its application for attribute selection in decision trees are discussed to emphasize knowledge-based modeling, simulation and data analysis in machine learning coherently. Guided by the perspectives and case studies of the digital thread, we present our recent work on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment.
Hybrid Electric Vehicle Battery-Ultracapacitor Energy Management System Design and Optimization

The widespread use of internal combustion engines (ICEs) to power vehicles has serious negative consequences for the environment and human health. Therefore, for three decades, changes in the automotive industry have been observed aimed at total or partial electrification of vehicle drive systems. The effect of these activities are successively introduced to the consumer market vehicles with hybrid (HEV) and electric (EV) drives. This article proposes an original version of the energy management system in a hybrid vehicle. The capabilities of a hybrid energy storage system (HESS) consisting of batteries and ultracapacitors units were explored through simulation. The most important parameters of the proposed strategy have been optimized. The simulation tests conducted confirmed many benefits of using an additional energy source. The applied changes in the energy management system reduced the level of fuel consumption, the maximum temperature, and the value of the charging and discharging currents of the batteries. This makes it possible to extend the service life of primary energy storage and reduce operating costs. It also has a positive effect on the environment, not only by reducing the emission of harmful effects of combustion but also by reducing the costs of disposal of used batteries.
Capabilities and organisational dimensions conducive to green product innovation: Evidence from Croatian and Spanish manufacturing firms

This paper aims to determine which configuration of green innovation capabilities (GICs) and organisational dimensions (ODs) leads to achieving green product innovation (GPI). We used data collected through the European Manufacturing Survey (EMS) from manufacturing firms in Spain and Croatia considered to be innovators. After conducting a cluster analysis, we identified a group of firms that still develop conventional product innovations (CPIs) and three groups of firms at different stages of GPI development. The four clusters were characterised using different variables, or determinants of GPI, associated with seven GICs and five ODs that favour GPI. According to the findings, all the GICs and ODs under analysis have a positive impact on GPI development, which results in the consolidation of a framework that organisations could use to manage green innovation. By empirically showing the relevance of applying these constructs, this study makes contributions to the Resource-Based Theory (RBT), along with its extension to GICs, and points to the need to associate them with the ODs to achieve GPI towards the challenge of sustainable development.
Trophic ecology of cats (Felis catus) in montaña de Guaza: implications for the conservation of the critically endangered giant lizard of Tenerife (Gallotia intermedia)

Cats have accompanied humans since they were domesticated, spreading and settling in a large number of islands where they modify environments and prey on native species. In the Canary Islands, several studies have shown that cat’s prey on native species, including Critically Endangered species such as the giant lizards of Tenerife (Gallotia intermedia), La Gomera (G. bravoana) and El Hierro (G. simonyi). This study focuses on the trophic ecology of the cat in Montaña de Guaza, where a population of the giant lizard of Tenerife is located. The current results show important changes in the diet of this alien species at this area in relation to the obtained by others author in 2004. The main changes detected are the low level of consumption of mammals and the high levels of birds and lizards. These changes seem to be a direct consequence of a low density of rabbit (Oryctolagus cuniculus), the main prey at the different habitats of Canary Islands, probably due to the rabbit hemorrhagic disease virus (RHDV) incidence. The high consumption of reptiles detected in this study could explain the reduction of both distribution area and number of giant lizards at this locality.
Effect of Aging on Corrosion Behavior of Martensite Phase in Cu-Al-Ni Shape Memory Alloy

Polarisation methods, and Open circuit potential measurements have been utilized to evaluate the impact of heat remediation on the corrosion characteristics of CuAlNi shape memory alloy in 3.5 percent NaCl solutions. CuAlNi alloy specimens were investigated in their as-sintered condition and following a thermal remediation processing that included annealing at 900 °C for 60 min associated with water quenching, and 200-degree centigrade for 30 hrs. and rapid cooling in iced water. The enhancement in polarisation resistance and reduction in corrosion rate of heat-treated CuAlNi alloy further suggests that heat remediation has a positive effect on CuAlNi alloy corrosion resistance. After measurements of polarisation, optical microscopy, SEM/EDX, and XRD examination of specimen surfaces reveal the presence of corrosion damage on the electrode surfaces, with CuCl2, AlCl3, and Cu2Cl (OH)3 compounds as surface corrosion products.
Predicting mechanical performance of starch-based foam materials

Variations in mixture proportions of plasticizers, additives, and crosslinking agents have significant impacts on mechanical performance of starch-based foam materials. In particular, starch/ethylene-vinyl acetate (EVA) foam materials have been developed with improved mechanical strength by optimizing the formulation. There is a lack of numerical correlations that could help analyze the effects of components and provide a predictive method for future research. In this study, we develop simple and accurate predictions for tensile strength and resilience based on mixture proportions of components for starch-based/EVA foam materials. The models constructed might be used to help design mixture proportions of starch-based foam materials. By combining optimization results from the Taguchi method and machine learning approaches, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time.
Flight to Quality in a Hierarchical Factor Model

We use a hierarchical factor model to study the behavior of yield curves derived from the market prices of corporate bonds issued by industrial companies and traded in the U.S. market. The sample is comprised by curves of different risk levels, which allows identifying four global forces and interpreting some of them as manifestations of flight to quality. The main contributions of this paper are detecting movements that can be assigned to flight to quality through a hierarchical factor model, an econometric tool not yet used in empirical studies about the phenomenon, and adding new insights to the literature about the driving factors of corporate curves.
Construction of Big Data Technology Training Environment for Vocational Education Based on Edge Computing Technology

With the rapid growth of the BD (big data) industry, many universities are focusing their professional development and expansion efforts on cultivating talent in related industries as well as building and developing big data disciplines. The BD technical training platform of vocational education based on IPE (Integration of Production and Education) is built in this paper, and the agreement signing, process management, effectiveness evaluation, big data analysis, and statistics of the integration of production and education are realized using edge computing technology, providing a solution for the informatization development of the integration of production and education. The access sequence was discovered among some knowledge points, as well as the sequence of knowledge points frequently viewed by students, and the teaching platform adjusts the content of web pages based on these rules to better provide personalized services for users.
A Low Complexity Symbol-Wise ML Detection Algorithm for User-Centric C-RAN

This letter considers an optimal signal detection problem for uplink user-centric cloud radio access network (C-RAN). In user-centric C-RAN, users communicate with nearby remote radio heads (RRHs), and RRHs are connected to baseband unit (BBU) pool. Then, the baseband processing functionalities are migrated to the BBU pool to provide joint signal detection for a number of users. However, joint signal processing will encounter high computational complexity as the network scale grows. Especially for optimal signal detection, the computational complexity increases exponentially as the number of users increases. To address this issue, we propose a symbol-wise maximum likelihood (ML) detection algorithm to achieve optimal performance with low complexity. The key to realize the symbol-by-symbol separate detection is that it makes full use of the sparsity of the channel matrix in user-centric C-RAN. We further provide the theoretical derivation to clarify the superiority of the proposed algorithm in this sparse channel scenario. Numerical experiments show that the proposed algorithm can achieve optimal performance with low complexity in user-centric C-RAN.
Correlation of radiological and histopathological response after neoadjuvant radiotherapy in soft tissue sarcoma

Abstract Background The aim of this study was to assess the association between radiological and histopathological response after neoadjuvant radiotherapy (nRT) in soft tissue sarcoma (STS), as well as the prognostic value of the different response evaluation methods on the oncological outcome. Methods A retrospective cohort of patients with localized STS of the extremity and trunk wall, treated with nRT followed by resection were included. The radiological response was assessed by RECIST 1.1 (RECIST) and MR-adapted Choi (Choi), histopathologic response was evaluated according to the EORTC-STBSG recommendations. Oncological outcome parameters of interest were local recurrence-free survival (LRFS), disease metastases-free survival (DMFS), and overall survival (OS). Results For 107 patients, complete pre- and postoperative pathology and imaging datasets were available. Most tumors were high-grade (77%) and the most common histological subtypes were undifferentiated pleomorphic sarcoma/not otherwise specified (UPS/NOS, 40%), myxoid liposarcoma (MLS, 21%) and myxofibrosarcoma (MFS, 16%). When comparing RECIST to Choi, the response was differently categorized in 58%, with a higher response rate (CR + PR) with Choi. Radiological responders showed a significant lower median percentage of viable cells (RECIST p = .050, Choi p = .015) and necrosis (RECIST p < .001), and a higher median percentage of fibrosis (RECIST p = .005, Choi p = .008), compared to radiological non-responders (SD + PD). RECIST, Choi, fibrosis, and viable cells were not significantly associated with altered oncological outcome, more necrosis was associated with poorer OS (p = .038). Conclusion RECIST, Choi and the EORTC-STBSG response score show incongruent results in response evaluation. The radiological response was significantly correlated with a lower percentage of viable cells and necrosis, but a higher percentage of fibrosis. Apart from necrosis, radiological nor other histopathological parameters were associated with oncologic outcomes.