Kraft Lignin-Based Polyols by Microwave: Optimizing Reaction Conditions

Microwave liquefaction of precipitated Kraft lignin was carried out in polyethylene glycol (PEG) and glycerol (G) mixed with or without H₂SO₄ as catalyst. The influences of some independent variables on the yield and hydroxyl index were discussed. The viscosity, molecular distribution (GPC), and the types of volatiles measured by gas chromatography–Mass spectrometry (GC-MS) of all the liquefied products were determined. Response surface methodology (RSM) was used to optimize liquefaction conditions. Based on the results, lignin/solvents (wt%), catalyst/solvents (wt%), and reaction time were chosen as independent variables for a central composite design (CCD). The optimal liquefaction conditions were as: 20 wt% of lignin, 3 wt% of catalyst at 5 min with yield and hydroxyl number of 95.27% and 537.95 mg KOH.g^?1, respectively. Functional groups (measured by ATR-IR [attenuated total reflectance – infrared]) and the thermal degradation (TGA) of optimized bio-polyol and precipitated kraft lignin were determined.     

Recycling of yttria‐stabilized zirconia waste powders in glazes suitable for ceramic tiles

The aim of this work is to demonstrate the feasibility of valorizing and recycling Yttria‐stabilized Zirconia thermal spray waste into high value products for industrial and residential use. Based on the powders chemistry and morphology, this work aims to realize products, like frits suitable for white glazes and ceramic tiles. The focus is on one class of powder: high‐temperature and abrasion‐resistant ceramics, like Yttria‐stabilized zirconia. This study has revealed that the substitution of pure zirconia with waste Yttria‐stabilized zirconia is possible in high percentages, up to 100% to prepare frits suitable for white glazes.     

Prognostic and Theranostic Applications of Positron Emission Tomography for a Personalized Approach to Metastatic Castration-Resistant Prostate Cancer

Metastatic castration-resistant prostate cancer (mCRPC) represents a condition of progressive disease in spite of androgen deprivation therapy (ADT), with a broad spectrum of manifestations ranging from no symptoms to severe debilitation due to bone or visceral metastatization. The management of mCRPC has been profoundly modified by introducing novel therapeutic tools such as antiandrogen drugs (i.e., abiraterone acetate and enzalutamide), immunotherapy through sipuleucel-T, and targeted alpha therapy (TAT). This variety of approaches calls for unmet need of biomarkers suitable for patients’ pre-treatment selection and prognostic stratification. In this scenario, imaging with positron emission computed tomography (PET/CT) presents great and still unexplored potential to detect specific molecular and metabolic signatures, some of whom, such as the prostate specific membrane antigen (PSMA), can also be exploited as therapeutic targets, thus combining diagnosis and therapy in the so-called “theranostic” approach. In this review, we performed a web-based and desktop literature research to investigate the prognostic and theranostic potential of several PET imaging probes, such as ¹⁸F-FDG, ¹⁸F-choline and ⁶⁸Ga-PSMA-11, also covering the emerging tracers still in a pre-clinical phase (e.g., PARP-inhibitors’ analogs and the radioligands binding to gastrin releasing peptide receptors/GRPR), highlighting their potential for defining personalized care pathways in mCRPC.     

Characterization of a Mouse Model of Alzheimer’s Disease Expressing Aβ4-42 and Human Mutant Tau

The relationship between the two most prominent neuropathological hallmarks of Alzheimer’s Disease (AD), extracellular amyloid-β (Aβ) deposits and intracellular accumulation of hyperphosphorylated tau in neurofibrillary tangles (NFT), remains at present not fully understood. A large body of evidence places Aβ upstream in the cascade of pathological events, triggering NFTs formation and the subsequent neuron loss. Extracellular Aβ deposits were indeed causative of an increased tau phosphorylation and accumulation in several transgenic models but the contribution of soluble Aβ peptides is still controversial. Among the different Aβ variants, the N-terminally truncated peptide Aβ_4–42 is among the most abundant. To understand whether soluble Aβ_4–42 peptides impact the onset or extent of tau pathology, we have crossed the homozygous Tg4–42 mouse model of AD, exclusively expressing Aβ_4–42 peptides, with the PS19 (P301S) tau transgenic model. Behavioral assessment showed that the resulting double-transgenic line presented a partial worsening of motor performance and spatial memory deficits in the aged group. While an increased loss of distal CA1 pyramidal neurons was detected in young mice, no significant alterations in hippocampal tau phosphorylation were observed in immunohistochemical analyses.     

Hemostatic gauze based on chitosan and hydroquinone: preparation,characterization and blood coagulation evaluation

This work concerns on the preparation and performance evaluation of a new chitosan hydroquinone based gauze for hemostatic use. Chitosan and hydroquinone were firstly connected by etherification and then linked to the pre-carboxylate gauze. The functionalized material and the chitosan-hydroquinone ether were characterized by Fourier Transform Infrared (FT-IR) Spectroscopy and Differential Scanning Calorimetry (DSC). FT-IR results showed that an esterification occurred on carboxylic group of the gauze. The gauze functionalization degree was also evaluated by volumetric analysis. The ether hydroquinone content was obtained by the Folin test. Moreover, the linkage between hydroquinone and chitosan was confirmed by nuclear magnetic resonance (NMR). The hemostatic activity of functionalized gauze was evaluated by dynamic blood clotting assays. The obtained results showed that the prepared material can shorten the blood clotting time and induce the adhesion and activation of platelets. Finally, swelling characteristic of the new gauze was evaluated to confirm its high capacity to absorb the blood.     

Collaborative network of firms: antecedents and state-of-the-art properties

The concept of collaborative networks has attracted considerable attention, especially in two knowledge domains: innovation and operation management. Although collaborative networks have evolved into many forms, their central role in triggering debates has not gone unnoticed. In this paper, we aim to contribute to these debates through a bibliometric review of a decade of research on collaborative networks (2002–2015). By using a combination of document co-citation and bibliographic coupling analyses, we unveil the origins and evolution of collaborative networks over time by analysing the references from the most important academic contributions. Our goal is threefold: first, to systematise the state of the art in the Input-Process-Output (IPO) framework; second, to elucidate the antecedents of the research field on collaborative networks (research streams originating from the field, main authors and journals) by organising them in six thematic clusters; and third, to show how the antecedents shaped the IPO framework. While ‘collaborative networks’ used to belong to two distinct domains, the research agendas seem to converge because of the interactions between their most active authors. As such, cross-fertilisation occurs with the International Journal of Production Research, which plays a central role in this process.     

A Novel Top‐Down Synthesis of Ultrathin 2D Boron Nanosheets for Multimodal Imaging‐Guided Cancer Therapy

Single atom nonmetal 2D nanomaterials have shown considerable potential in cancer nanomedicines, owing to their intriguing properties and biocompatibility. Herein, ultrathin boron nanosheets (B NSs) are prepared through a novel top‐down approach by coupling thermal oxidation etching and liquid exfoliation technologies, with controlled nanoscale thickness. Based on the PEGylated B NSs, a new photonic drug delivery platform is developed, which exhibits multiple promising features for cancer therapy and imaging, including: i) efficient NIR‐light‐to‐heat conversion with a high photothermal conversion efficiency of 42.5%, ii) high drug‐loading capacity and triggered drug release by NIR light and moderate acidic pH, iii) strong accumulation at tumor sites, iv) multimodal imaging properties (photoacoustic, photothermal, and fluorescence imaging), and v) complete tumor ablation and excellent biocompatibility. As far as it is known, this is the first report on the top‐down fabrication of ultrathin 2D B NSs by the combined thermal oxidation etching and liquid exfoliation, as well as their application as a multimodal imaging‐guided drug delivery platform. The newly prepared B NSs are also expected to provide a robust and useful 2D nanoplatform for various biomedical applications.     

Delayed renal dysfunction and flash pulmonary edema post endovascular abdominal aneurysm repair

After endovascular aortic repair (EVAR), the deterioration in long‐term renal function is probably multifactorial. Preoperative renal failure is an independent risk factor. Postoperative renal dysfunction can be associated with inadvertent renal artery occlusion, renal artery complications as stenosis, plaque dislodgement, or dissection. Ischemic nephropathy can accelerate hypertension and circulatory congestion. We report a case of coverage of the renal arteries symptomatic with flash pulmonary edema and renal failure 15 months after EVAR, suggesting a delayed endograft migration. The patient had complete resolution of symptoms and renal function after renal artery stenting with placement between endograft and aneurysm to the left renal artery.     

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.     

Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble Method

Dimensionality reduction (DR) has been one central research topic in information theory, pattern recognition, and machine learning. Apparently, the performance of many learning models significantly rely on dimensionality reduction: successful DR can largely improve various approaches in clustering and classification, while inappropriate DR may deteriorate the systems. When applied on high-dimensional data, some existing research approaches often try to reduce the dimensionality first, and then input the reduced features to other available models, e.g., Gaussian mixture model (GMM). Such independent learning could however significantly limit the performance, since the optimal subspace given by a particular DR approach may not be appropriate for the following model. In this paper, we focus on investigating how unsupervised dimensionality reduction could be performed together with GMM and if such joint learning could lead to improvement in comparison with the traditional unsupervised method. In particular, we engage the mixture of factor analyzers with the assumption that a common factor loading exists for all the components. Based on that, we then present EM-algorithm that converges to a local optimal solution. Such setting exactly optimizes a dimensionality reduction together with the parameters of GMM. We describe the framework, detail the algorithm, and conduct a series of experiments to validate the effectiveness of our proposed approach. Specifically, we compare the proposed joint learning approach with two competitive algorithms on one synthetic and six real data sets. Experimental results show that the joint learning significantly outperforms the comparison methods in terms of three criteria.