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Photosynthetic pigment composition has been a major study target in plant ecophysiology during the last three decades. Although more than 2000 papers have been published, a comprehensive evaluation of the responses of photosynthetic pigment composition to environmental conditions is not yet available. After an extensive survey, we compiled data from 525 papers including 809 species (subkingdom Viridiplantae) in which pigment composition was described. A meta‐analysis was then conducted to assess the ranges of photosynthetic pigment content. Calculated frequency distributions of pigments were compared with those expected from the theoretical pigment composition. Responses to environmental factors were also analysed. The results revealed that lutein and xanthophyll cycle pigments (VAZ) were highly responsive to the environment, emphasizing the high phenotypic plasticity of VAZ, whereas neoxanthin was very stable. The present meta‐analysis supports the existence of relatively narrow limits for pigment ratios and also supports the presence of a pool of free ‘unbound’ VAZ. Results from this study provide highly reliable ranges of photosynthetic pigment contents as a framework for future research on plant pigments.

The increasing cost of drug development has raised the demand for surrogate endpoints when evaluating new drugs in clinical trials. However, over the years, it has become clear that surrogate endpoints need to be statistically evaluated and deemed valid, before they can be used as substitutes of “true” endpoints in clinical studies. Nowadays, two paradigms, based on causal‐inference and meta‐analysis, dominate the scene. Nonetheless, although the literature emanating from these paradigms is wide, till now the relationship between them has largely been left unexplored. In the present work, we discuss the conceptual framework underlying both approaches and study the relationship between them using theoretical elements and the analysis of a real case study. Furthermore, we show that the meta‐analytic approach can be embedded within a causal‐inference framework on the one hand and that it can be heuristically justified why surrogate endpoints successfully evaluated using this approach will often be appealing from a causal‐inference perspective as well, on the other. A newly developed and user friendly R package Surrogate is provided to carry out the evaluation exercise.

In the past decade plant geneticists began using complex plant populations to identify QTL by association analysis, and the practice is becoming commonplace. Plant populations present unique challenges for association analyses. Plant populations vary in complexity and structure and analyses generally derived from human genetics have been applied to them in a broad fashion. We review analytical techniques and their application in different plant populations. Analyses were classified as either family-based (FBAA) or population-based (PBAA). Over time, the different analyses have been generalized to accommodate a variety of populations, and are complementary. The PBAA are suited for populations with individuals that share little ancestry. Use of PBAA in these types of populations has dominated plant association analyses with success, though PBAA is unlikely to detect some important QTL in highly structured populations. Both PBAA and FBAA are suited for populations of related individuals. The use of FBAA in a breeding population warrants special attention due to features such as large population size, availability of phenotypic data, immediate relevance to marker-assisted selection, ease of QTL validation, and the computational simplicity of tests that require linkage for significance. Specific recommendations for PBAA and FBAA are made as well as some suggestions for future directions of research.

Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of information indispensable for understanding, modelling, diagnosis, and treatment of diseases. Reconstruction algorithms entail transforming signals collected by acquisition hardware into interpretable images. Reconstruction is a challenging task given the ill-posedness of the problem and the absence of exact analytic inverse transforms in practical cases. While the last decades witnessed impressive advancements in terms of new modalities, improved temporal and spatial resolution, reduced cost, and wider applicability, several improvements can still be envisioned such as reducing acquisition and reconstruction time to reduce patient’s exposure to radiation and discomfort while increasing clinics throughput and reconstruction accuracy. Furthermore, the deployment of biomedical imaging in handheld devices with small power requires a fine balance between accuracy and latency. The design of fast, robust, and accurate reconstruction algorithms is a desirable, yet challenging, research goal. While the classical image reconstruction algorithms approximate the inverse function relying on expert-tuned parameters to ensure reconstruction performance, deep learning (DL) allows automatic feature extraction and real-time inference. Hence, DL presents a promising approach to image reconstruction with artifact reduction and reconstruction speed-up reported in recent works as part of a rapidly growing field. We review state-of-the-art image reconstruction algorithms with a focus on DL-based methods. First, we examine common reconstruction algorithm designs, applied metrics, and datasets used in the literature. Then, key challenges are discussed as potentially promising strategic directions for future research.

In recent years, interest in economic, environmental and social sustainability has increased significantly. Companies are gradually adopting behaviors aimed at achieving the Sustainable Development Goals, which represent a crucial aspect of the 2030 Agenda. In practice, they are currently incorporating organizational strategies that jointly consider environmental, social and corporate governance (ESG), with the aim of generating value for all stakeholders. This paper aims to review, through a recognized seven-step procedure, the current literature on the impact that ESG practices have in industry, with a focus on the reduction of carbon emissions. The results are extremely useful for both researchers and entrepreneurs. The bibliometric analysis shows that interest in the ESG paradigm has grown considerably in the last three years. Furthermore, through the analysis of 13 key documents, it emerges that (i) the European community is pushing significantly towards the adoption of ESG practices through new regulations, (ii) the link between industrial operations and carbon emissions can no longer be neglected within the factory of the future, and (iii) significant efforts are still needed to standardize, in terms of variables and KPIs, the adoption of ESG-centric strategies.

The present research article focuses on an analytically based method for the optimal allocation and sizing of a renewable energy source (RES) capable of injecting both active and reactive powers in the distribution network. The placement of distributed generation (DG) in the distribution network reduces the magnitude of branch current in between the reference bus and the bus where DG is to be installed. Due to this, system power loss decreases significantly. The proposed method considers different levels of load in addition to peak load demand. The goal of the developed method is to minimize system losses by optimal DG allocations. In the proposed method, the optimum size of the DG is obtained on the basis of maximum loss saving criterion. For the execution of proposed method, only a base case load flow solution is required. The developed method has been tested on IEEE 69-bus and 33-bus radial distribution networks. On the basis of obtained results, it has been realized that the developed method is more capable of diminishing system energy losses.

Hydrothermal vent fields in the western Pacific Ocean are mostly distributed along spreading centers in submarine basins behind convergent plate boundaries. Larval dispersal resulting from deep-ocean circulations is one of the major factors influencing gene flow, diversity, and distributions of vent animals. By combining a biophysical model and deep-profiling float experiments, we quantify potential larval dispersal of vent species via ocean circulation in the western Pacific Ocean. We demonstrate that vent fields within back-arc basins could be well connected without particular directionality, whereas basin-to-basin dispersal is expected to occur infrequently, once in tens to hundreds of thousands of years, with clear dispersal barriers and directionality associated with ocean currents. The southwest Pacific vent complex, spanning more than 4,000 km, may be connected by the South Equatorial Current for species with a longer-than-average larval development time. Depending on larval dispersal depth, a strong western boundary current, the Kuroshio Current, could bridge vent fields from the Okinawa Trough to the Izu-Bonin Arc, which are 1,200 km apart. Outcomes of this study should help marine ecologists estimate gene flow among vent populations and design optimal marine conservation plans to protect one of the most unusual ecosystems on Earth.

This study discusses the development and validation of a universal microwell spectrophotometric assay for TKIs, regardless of the diversity in their chemical structures. The assay depends on directly measuring the native ultraviolet light (UV) absorption of TKIs. The assay was carried out using UV-transparent 96-microwell plates and the absorbance signals were measured by a microplate reader at 230 nm, at which all TKIs had light absorption. Beer’s law correlating the absorbances of TKIs with their corresponding concentrations was obeyed in the range of 2–160 µg mL–1 with excellent correlation coefficients (0.9991–0.9997). The limits of detection and limits quantitation were in the ranges of 0.56–5.21 and 1.69–15.78 µg mL–1, respectively. The proposed assay showed high precision as the values of the relative standard deviations for the intra- and inter-assay precisions did not exceed 2.03 and 2.14%, respectively. The accuracy of the assay was proven as the recovery values were in the range of 97.8–102.9% (±0.8–2.4%). The proposed assay was successfully applied to the quantitation of all TKIs in their pharmaceutical formulations (tablets) with reliable results in terms of high accuracy and precision. The assay greenness was evaluated, and the results proved that the assay fulfils the requirements of green analytical approach. The proposed assay is the first assay that can analyse all TKIs on a single assay system without chemical derivatization or modifications in the detection wavelength. In addition, the simple and simultaneous handling of a large number of samples as a batch using micro-volumes of samples gave the assay the advantage of high throughput analysis, which is a serious demand in the pharmaceutical industry.

This research aimed to assess the adsorption properties of raw walnut shell powder (WNSp) for the elimination of methylene blue (MB) from an aqueous medium. The initial MB concentration (2–50 mg/L), the mass of the biomaterial (0.1–1 g/L), the contact time (10–120 min), the medium’s pH (2–12), and the temperature (25–55 °C) were optimized as experimental conditions. A maximum adsorption capacity of 19.99 mg/g was obtained at an MB concentration of 50 mg/L, a medium pH of 6.93 and a temperature of 25 °C, using 0.2 g/L of WNSp. These conditions showed that the MB dye elimination process occurred spontaneously. Different analytical approaches were used to characterize the WNSp biomaterial, including functional groups involved in MB adsorption, the surface characteristics and morphological features of the WNSp before and after MB uptake, and identification of WNSp based on their diffraction pattern. The experimental isotherm data were analyzed by the Langmuir and Freundlich models for the adsorption of MB dye. The corresponding values of parameter RL of Langmuir were between 0.51 and 0.172, which confirmed the WNSp’s favorable MB dye adsorption. The experimental kinetic data were examined, and the pseudo-second-order model was shown to be more suitable for describing the adsorption process, with an excellent determination coefficient (R2 = 0.999). The exchanged standard enthalpy (H° = −22.456 KJ.mol−1) was calculated using the van ‘t Hoff equation, and it was proven that the adsorption process was exothermic. The spontaneous nature and feasibility of the MB dye adsorption process on WNSp were validated by negative standard enthalpy values (G°) ranging from −2.580 to −0.469 at different temperatures. It was established that WNSp may be employed as a novel, effective, low-cost adsorbent for the elimination of methylene blue in aqueous solutions.

Research and development on composite materials has increased significantly due to the current and growing need for lighter, newer, fuel-efficient materials with improved stiffness, strength-to-weight ratios, and dependability.Especially in automotive vehicles, now days, 70% to 80% parts of conventional materials are replaced by composites due to its high specific strength. The transmission component of a vehicle plays a major role in weight reduction. The goal of the proposed research project is to design and construct a carbon fiber-reinforced nylon 66 composite drive shaft for a passenger car. To develop and optimize the composite drive shaft, an analytical technique is used. Modeling and analysis are conducted for validation. In addition, experimental investigation is also carried out for the testing of its performance.