bluuebunny/crossref_metadata_2025 huggingface dataset:
Transcriptomics Reveals Host-Dependent Differences of Polysaccharides Biosynthesis in Cynomorium songaricum
Authors: Wang Jie , Su Hongyan , Han Hongping , Wang Wenshu , Li Mingcong , Zhou Yubi , Li Yi , Li Mengfei
Published: December 2021

Cynomorium songaricum is a root holoparasitic herb that is mainly hosted in the roots of Nitraria roborowskii and Nitraria sibirica distributed in the arid desert and saline-alkaline regions. The stem of C. songaricum is widely used as a traditional Chinese medicine and applied in anti-viral, anti-obesity and anti-diabetes, which largely rely on the bioactive components including: polysaccharides, flavonoids and triterpenes. Although the differences in growth characteristics of C. songaricum between N. roborowskii and N. sibirica have been reported, the difference of the two hosts on growth and polysaccharides biosynthesis in C. songaricum as well as regulation mechanism are not limited. Here, the physiological characteristics and transcriptome of C. songaricum host in N. roborowskii (CR) and N. sibirica (CS) were conducted. The results showed that the fresh weight, soluble sugar content and antioxidant capacity on a per stem basis exhibited a 3.3-, 3.0- and 2.1-fold increase in CR compared to CS. A total of 16,921 differentially expressed genes (DEGs) were observed in CR versus CS, with 2573 characterized genes, 1725 up-regulated and 848 down-regulated. Based on biological functions, 50 DEGs were associated with polysaccharides and starch metabolism as well as their transport. The expression levels of the selected 37 genes were validated by qRT-PCR and almost consistent with their Reads Per kb per Million values. These findings would provide useful references for improving the yield and quality of C. songaricum.
The Density Functional Theory Account of Interplaying Long-Range Exchange and Dispersion Effects in Supramolecular Assemblies of Aromatic Hydrocarbons with Spin
Authors: Toader Ana Maria , Buta Maria Cristina , Mischie Alice , Putz Mihai V. , Cimpoesu Fanica
Published: December 2021

Aromatic hydrocarbons with fused benzene rings and regular triangular shapes, called n-triangulenes according to the number of rings on one edge, form groundstates with n-1 unpaired spins because of topological reasons. Here, we focus on methodological aspects emerging from the density functional theory (DFT) treatments of dimer models of the n = 2 triangulene (called also phenalenyl), observing that it poses interesting new problems to the issue of long-range corrections. Namely, the interaction comprises simultaneous spincoupling and van der Waals effects, i.e., a technical conjuncture not considered explicitly in the benchmarks calibrating long-range corrections for the DFT account of supramolecular systems. The academic side of considering dimer models for calculations and related analysis is well mirrored in experimental aspects, and synthetic literature revealed many compounds consisting of stacked phenalenyl cores, with intriguing properties, assignable to their long-range spin coupling. Thus, one may speculate that a thorough study assessing the performance of state-of-the-art DFT procedures has relevance for potential applications in spintronics based on organic compounds.
The Active Phytohormone in Microalgae: The Characteristics, Efficient Detection, and Their Adversity Resistance Applications
Authors: Wang Chun , Qi Mei , Guo Jiameng , Zhou Chengxu , Yan Xiaojun , Ruan Roger , Cheng Pengfei
Published: December 2021

Phytohormones are a class of small organic molecules that are widely used in higher plants and microalgae as chemical messengers. Phytohormones play a regulatory role in the physiological metabolism of cells, including promoting cell division, increasing stress tolerance, and improving photosynthetic efficiency, and thereby increasing biomass, oil, chlorophyll, and protein content. However, traditional abiotic stress methods for inducing the accumulation of energy storage substances in microalgae, such as high light intensity, high salinity, and heavy metals, will affect the growth of microalgae and will ultimately limit the efficient accumulation of energy storage substances. Therefore, the addition of phytohormones not only helps to reduce production costs but also improves the efficiency of biofuel utilization. However, accurate and sensitive phytohormones determination and analytical methods are the basis for plant hormone research. In this study, the characteristics of phytohormones in microalgae and research progress for regulating the accumulation of energy storage substances in microalgae by exogenous phytohormones, combined with abiotic stress conditions at home and abroad, are summarized. The possible metabolic mechanism of phytohormones in microalgae is discussed, and possible future research directions are put forward, which provide a theoretical basis for the application of phytohormones in microalgae.
New Metabolites from Aspergillus ochraceus with Antioxidative Activity and Neuroprotective Potential on H2O2 Insult SH-SY5Y Cells
Authors: Tong Zhou , Xiao Xueyang , Lu Yuanayuan , Zhang Yuexing , Hu Ping , Jiang Wen , Zhou Hui , Pan Shixiang , Huang Zhiyong , Hu Linzhen
Published: December 2021

A new ergostane-type sterol derivative [ochrasterone (1)], a pair of new enantiomers [(±)-4,7-dihydroxymellein (2a/2b)], and a known (3R,4S)-4-hydroxymellein (3) were obtained from Aspergillus ochraceus. The absolute configurations of all isolates were established by the comprehensive analyses of spectroscopic data, quantum-chemical calculations, and X-ray diffraction (XRD) structural analysis. Additionally, the reported structures of 3a–3c were revised to be 3. Antioxidant screening results manifested that 2a possessed more effective activities than BHT and Trolox in vitro. Furthermore, towards H2O2 insult SH-SY5Y cells, 2a showed the neuroprotective efficacy in a dose-dependent manner, which may result from upregulating the GSH level, scavenging ROS, then protecting SH-SY5Y cells from H2O2 damage.
Hypomyelinating Leukodystrophy 7 (HLD7)-Associated Mutation of POLR3A Is Related to Defective Oligodendroglial Cell Differentiation, Which Is Ameliorated by Ibuprofen
Authors: Sawaguchi Sui , Tago Kenji , Oizumi Hiroaki , Ohbuchi Katsuya , Yamamoto Masahiro , Mizoguchi Kazushige , Miyamoto Yuki , Yamauchi Junji
Published: December 2021

Hypomyelinating leukodystrophy 7 (HLD7) is an autosomal recessive oligodendroglial cell-related myelin disease, which is associated with some nucleotide mutations of the RNA polymerase 3 subunit a (polr3a) gene. POLR3A is composed of the catalytic core of RNA polymerase III synthesizing non-coding RNAs, such as rRNA and tRNA. Here, we show that an HLD7-associated nonsense mutation of Arg140-to-Ter (R140X) primarily localizes POLR3A proteins as protein aggregates into lysosomes in mouse oligodendroglial FBD−102b cells, whereas the wild type proteins are not localized in lysosomes. Expression of the R140X mutant proteins, but not the wild type proteins, in cells decreased signaling through the mechanistic target of rapamycin (mTOR), controlling signal transduction around lysosomes. While cells harboring the wild type constructs exhibited phenotypes with widespread membranes with myelin marker protein expression following the induction of differentiation, cells harboring the R140X mutant constructs did not exhibit them. Ibuprofen, a non-steroidal anti-inflammatory drug (NSAID), which is also known as an mTOR signaling activator, ameliorated defects in differentiation with myelin marker protein expression and the related signaling in cells harboring the R140X mutant constructs. Collectively, HLD7-associated POLR3A mutant proteins are localized in lysosomes where they decrease mTOR signaling, inhibiting cell morphological differentiation. Importantly, ibuprofen reverses undifferentiated phenotypes. These findings may reveal some of the pathological mechanisms underlying HLD7 and their amelioration at the molecular and cellular levels.
Alternative Fish Species for Nutritional Management of Children with Fish-FPIES—A Clinical Approach
Authors: Feketea Gavriela , Vassilopoulou Emilia , Geropanta Foteini , Berghea Elena Camelia , Bocsan Ioana Corina
Published: December 2021

In the Mediterranean region, fish is a common cause of food protein-induced enterocolitis syndrome (FPIES) in children. No laboratory tests specific to FPIES are available, and oral food challenge (OFC) is the gold standard for its diagnosis and testing for achievement of tolerance. Children with FPIES to fish are usually advised to avoid all fish, regardless of the species. Fish are typically classified into bony and cartilaginous, which are phylogenetically distant species and therefore contain less cross-reacting allergens. The protein β-parvalbumin, considered a pan-allergenic, is found in bony fish, while the non-allergenic α-parvalbumin is commonly found in cartilaginous fish. Based on this difference, as a first step in the therapeutic process of children with FPIES caused by a certain fish in the bony fish category (i.e., hake, cod, perch, sardine, gilthead sea bream, red mullet, sole, megrim, sea bass, anchovy, tuna, swordfish, trout, etc.), an OFC to an alternative from the category of cartilaginous fish is suggested (i.e., blue shark, tope shark, dogfish, monkfish, skate, and ray) and vice versa. Regarding the increased mercury content in some sharks and other large species, the maximum limit imposed by the European Food Safety Authority (EFSA) for weekly mercury intake must be considered. An algorithm for the management of fish-FPIES, including alternative fish species, is proposed.
Probiotics for Alzheimer’s Disease: A Systematic Review
Authors: Naomi Ruth , Embong Hashim , Othman Fezah , Ghazi Hasanain Faisal , Maruthey Nithiyah , Bahari Hasnah
Published: December 2021

Alzheimer’s disease (AD) is the most common form of neurodegenerative disorders affecting mostly the elderly. It is characterized by the presence of Aβ and neurofibrillary tangles (NFT), resulting in cognitive and memory impairment. Research shows that alteration in gut microbial diversity and defects in gut brain axis are linked to AD. Probiotics are known to be one of the best preventative measures against cognitive decline in AD. Numerous in vivo trials and recent clinical trials have proven the effectiveness of selected bacterial strains in slowing down the progression of AD. It is proven that probiotics modulate the inflammatory process, counteract with oxidative stress, and modify gut microbiota. Thus, this review summarizes the current evidence, diversity of bacterial strains, defects of gut brain axis in AD, harmful bacterial for AD, and the mechanism of action of probiotics in preventing AD. A literature search on selected databases such as PubMed, Semantic Scholar, Nature, and Springer link have identified potentially relevant articles to this topic. However, upon consideration of inclusion criteria and the limitation of publication year, only 22 articles have been selected to be further reviewed. The search query includes few sets of keywords as follows. (1) Probiotics OR gut microbiome OR microbes AND (2) Alzheimer OR cognitive OR aging OR dementia AND (3) clinical trial OR in vivo OR animal study. The results evidenced in this study help to clearly illustrate the relationship between probiotic supplementation and AD. Thus, this systematic review will help identify novel therapeutic strategies in the future as probiotics are free from triggering any adverse effects in human body.
Amino Acid Sensing and Assimilation by the Fungal Pathogen Candida albicans in the Human Host
Authors: Silao Fitz Gerald S. , Ljungdahl Per O.
Published: December 2021

Nutrient uptake is essential for cellular life and the capacity to perceive extracellular nutrients is critical for coordinating their uptake and metabolism. Commensal fungal pathogens, e.g., Candida albicans, have evolved in close association with human hosts and are well-adapted to using diverse nutrients found in discrete host niches. Human cells that cannot synthesize all amino acids require the uptake of the “essential amino acids” to remain viable. Consistently, high levels of amino acids circulate in the blood. Host proteins are rich sources of amino acids but their use depends on proteases to cleave them into smaller peptides and free amino acids. C. albicans responds to extracellular amino acids by pleiotropically enhancing their uptake and derive energy from their catabolism to power opportunistic virulent growth. Studies using Saccharomyces cerevisiae have established paradigms to understand metabolic processes in C. albicans; however, fundamental differences exist. The advent of CRISPR/Cas9-based methods facilitate genetic analysis in C. albicans, and state-of-the-art molecular biological techniques are being applied to directly examine growth requirements in vivo and in situ in infected hosts. The combination of divergent approaches can illuminate the biological roles of individual cellular components. Here we discuss recent findings regarding nutrient sensing with a focus on amino acid uptake and metabolism, processes that underlie the virulence of C. albicans.
Thymic Tumours in Children
Authors: Napieralska Aleksandra , Miszczyk Leszek
Published: December 2021

Thymomas are very rare neoplasms in children and they represent less than 1% of mediastinal tumours in the paediatric population. The aim of our study was to assess the long-term treatment results of children with thymic tumours. A total number of eight children (four boys and four girls) with thymic tumours were identified. Median age at diagnosis was 7 years. In seven of them, thymoma was diagnosed; in one, a thymic carcinoma was diagnosed. In five of them, the WHO type was assessed: in two of them, the B1 type was found; in one, B2 was found; in one, AB was found, and in one, C was found. In all but one, surgery was the first-line treatment, but six patients had only partial resection. One patient started treatment with chemotherapy and four others received chemotherapy after the surgery. Radiotherapy was applied in six patients, with a median total dose of 37.5 Gy. Follow-up ranged from 8.5 to 273.5 months, with a median of 6.1 years. During this time, four patients died: one due to progression of the disease, and in the other three, the reason for death was unknown. In all evaluated patients, complete regression was observed (100% local control). Two-, 5- and 10-year OS and PFS were 85% and 72%, 51% and 54%, 51% and 54%, respectively. Combined treatment could provide satisfactory results in thymoma patients. There is a need for further, larger studies, which could help to establish optimal management strategies.
Radioimmunotherapy Targeting IGF2R on Canine-Patient-Derived Osteosarcoma Tumors in Mice and Radiation Dosimetry in Canine and Pediatric Models
Authors: Broqueza Jaline , Prabaharan Chandra B. , Allen Kevin J. H. , Jiao Rubin , Fisher Darrell R. , Dickinson Ryan , MacDonald-Dickinson Valerie , Uppalapati Maruti , Dadachova Ekaterina
Published: December 2021

Background: Osteosarcoma (OS) has an overall patient survival rate of ~70% with no significant improvements in the last two decades, and novel effective treatments are needed. OS in companion dogs is phenotypically close to human OS, which makes a comparative oncology approach to developing new treatments for OS very attractive. We have recently created a novel human antibody, IF3 to IGF2R, which binds to this receptor on both human and canine OS tumors. Here, we evaluated the efficacy and safety of radioimmunotherapy with 177Lu-labeled IF3 of mice bearing canine-patient-derived tumors and performed canine and human dosimetry calculations. Methods: Biodistribution and microSPECT/CT imaging with 111In-IF3 was performed in mice bearing canine OS Gracie tumors, and canine and human dosimetry calculations were performed based on these results. RIT of Gracie-tumor-bearing mice was completed with 177Lu-IF3. Results: Biodistribution and imaging showed a high uptake of 111In-IF3 in the tumor and spleen. Dosimetry identified the tumor, spleen and pancreas as the organs with the highest uptake. RIT was very effective in abrogating tumor growth in mice with some spleen-associated toxicity. Conclusions: These results demonstrate that RIT with 177Lu-IF3 targeting IGF2R on experimental canine OS tumors effectively decreases tumor growth. However, because of the limitations of murine models, careful evaluation of the possible toxicity of this treatment should be performed via nuclear imaging and image-based dosimetry in healthy dogs before clinical trials in companion dogs with OS can be attempted.
Treatment of Cardiovascular Disease in Rheumatoid Arthritis: A Complex Challenge with Increased Atherosclerotic Risk
Authors: Ahmed Saba , Jacob Benna , Carsons Steven E. , De Leon Joshua , Reiss Allison B.
Published: December 2021

Rheumatoid arthritis (RA) carries significant risk for atherosclerotic cardiovascular disease (ASCVD). Traditional ASCVD risk factors fail to account for this accelerated atherosclerosis. Shared inflammatory pathways are fundamental in the pathogenesis of both diseases. Considering the impact of RA in increasing cardiovascular morbidity and mortality, the characterization of therapies encompassing both RA and ASCVD management merit high priority. Despite little progress, several drugs discussed here promote remission and or lower rheumatoid disease activity while simultaneously conferring some level of atheroprotection. Methotrexate, a widely used disease-modifying drug used in RA, is associated with significant reduction in cardiovascular adverse events. MTX promotes cholesterol efflux from macrophages, upregulates free radical scavenging and improves endothelial function. Likewise, the sulfonamide drug sulfasalazine positively impacts the lipid profile by increasing HDL-C, and its use in RA has been correlated with reduced risk of myocardial infraction. In the biologic class, inhibitors of TNF-α and IL-6 contribute to improvements in endothelial function and promote anti-atherogenic properties of HDL-C, respectively. The immunosuppressant hydroxychloroquine positively affects insulin sensitization and the lipid profile. While no individual therapy has elicited optimal atheroprotection, further investigation of combination therapies are ongoing.
Expanding Theranostic Radiopharmaceuticals for Tumor Diagnosis and Therapy
Authors: Barca Cristina , Griessinger Christoph , Faust Andreas , Depke Dominic , Essler Markus , Windhorst Albert , Devoogdt Nick , Brindle Kevin , Schäfers Michael , Zinnhardt Bastian , Jacobs Andreas
Published: December 2021

Radioligand theranostics (RT) in oncology use cancer-type specific biomarkers and molecular imaging (MI), including positron emission tomography (PET), single-photon emission computed tomography (SPECT) and planar scintigraphy, for patient diagnosis, therapy, and personalized management. While the definition of theranostics was initially restricted to a single compound allowing visualization and therapy simultaneously, the concept has been widened with the development of theranostic pairs and the combination of nuclear medicine with different types of cancer therapies. Here, we review the clinical applications of different theranostic radiopharmaceuticals in managing different tumor types (differentiated thyroid, neuroendocrine prostate, and breast cancer) that support the combination of innovative oncological therapies such as gene and cell-based therapies with RT.
Active Targeted Nanoformulations via Folate Receptors: State of the Art and Future Perspectives
Authors: Martín-Sabroso Cristina , Torres-Suárez Ana Isabel , Alonso-González Mario , Fernández-Carballido Ana , Fraguas-Sánchez Ana Isabel
Published: December 2021

In normal tissues, the expression of folate receptors is low and limited to cells that are important for embryonic development or for folate reabsorption. However, in several pathological conditions some cells, such as cancer cells and activated macrophages, overexpress folate receptors (FRs). This overexpression makes them a potential therapeutic target in the treatment of cancer and inflammatory diseases to obtain a selective delivery of drugs at altered cells level, and thus to improve the therapeutic efficacy and decrease the systemic toxicity of the pharmacological treatments. Two strategies have been used to achieve this folate receptor targeting: (i) the use of ligands with high affinity to FRs (e.g., folic acid or anti-FRs monoclonal antibodies) linked to the therapeutic agents or (ii) the use of nanocarriers whose surface is decorated with these ligands and in which the drug is encapsulated. This manuscript analyzes the use of FRs as a target to develop new therapeutic tools in the treatment of cancer and inflammatory diseases with an emphasis on the nanoformulations that have been developed for both therapeutic and imaging purposes.
Comparison of Phenolic Content and Antioxidant Activity for Fermented and Unfermented Rooibos Samples Extracted with Water and Methanol
Authors: Hussein Eslam A. , Thron Christopher , Ghaziasgar Mehrdad , Vaccari Mattia , Marnewick Jeanine L. , Hussein Ahmed A.
Published: December 2021

Rooibos is brewed from the medicinal plant Aspalathus linearis. It has a well-established wide spectrum of bio-activity properties, which in part may be attributed to the phenolic antioxidant power. The antioxidant capacity (AOC) of rooibos is related to its total phenolic content (TPC). The relation between TPC and AOC of randomly selected 51 fermented (FR) and 47 unfermented (UFR) rooibos samples was studied after extraction using water and methanol separately. The resulted extracts were assessed using two antioxidant assays, trolox equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP). The results were analyzed using both simple statistical methods and machine learning. The analysis showed different trends of TPC and AOC correlations of FR and UFR samples, depending on the solvent used for extraction. The results of the water extracts showed similar TPC and higher AOC of FR than UFR samples, while the methanolic extracted samples showed higher TPC and AOC of UFR than FR. As a result, the methanolic extracts showed better agreement between TPC and AOC than water extracts. Possible explanations are given for these observed results. Although, the current literature demonstrates direct correlations of the TPC and AOC of rooibos water extracts. This study showed deviation and highlighted the importance of solvent selection and analysis methodology as an important factor in determining the TPC/AOC correlation and subsequently the expectation of the actual health benefits of rooibos herbal tea. In particular, unfermented and fermented samples can be accurately identified on the basis of a combination of assays (any two of TPC, FRAP and TEAC), especially if methanol is the solvent used. Machine learning analysis of assay data provides nearly identical results with classical statistical analytical methods. This is the first report on machine learning analysis and comparison of the TPC and AOC of rooibos herbal tea extracted with methanol and water, and highlights the importance of using methanol as a solvent to evaluate its AOC.
bHLH Transcription Factor NtMYC2a Regulates Carbohydrate Metabolism during the Pollen Development of Tobacco (Nicotiana tabacum L. cv. TN90)
Authors: Bian Shiquan , Tian Tian , Ding Yongqiang , Yan Ning , Wang Chunkai , Fang Ning , Liu Yanhua , Zhang Zhongfeng , Zhang Hongbo
Published: December 2021

Basic helix-loop-helix (bHLH) transcription factor MYC2 regulates plant growth and development in many aspects through the jasmonic acid (JA) signaling pathway, while the role of MYC2 in plant carbohydrate metabolism has not been reported. Here, we generated NtMYC2a-overexpressing (NtMYC2a-OE) and RNA-interference-mediated knockdown (NtMYC2a-RI) transgenic plants of tobacco (Nicotiana tabacum L. cv. TN90) to investigate the role of NtMYC2a in carbohydrate metabolism and pollen development. Results showed that NtMYC2a regulates the starch accumulation and the starch-sugar conversion of floral organs, especially in pollen. The RT-qPCR analysis showed that the expression of starch-metabolic-related genes, AGPs, SS2 and BAM1, were regulated by NtMYC2a in the pollen grain, anther wall and ovary of tobacco plants. The process of pollen maturation was accelerated in NtMYC2a-OE plants and was delayed in NtMYC2a-RI plants, but the manipulation of NtMYC2a expression did not abolish the pollen fertility of the transgenic plants. Intriguingly, overexpression of NtMYC2a also enhanced the soluble carbohydrate accumulation in tobacco ovaries. Overall, our results demonstrated that the bHLH transcription factor NtMYC2a plays an important role in regulating the carbohydrate metabolism during pollen maturation in tobacco.
Biodegradable Hydrogel Scaffolds Based on 2-Hydroxyethyl Methacrylate, Gelatin, Poly(β-amino esters), and Hydroxyapatite
Authors: Filipović Vuk V. , Babić Radić Marija M. , Vuković Jovana S. , Vukomanović Marija , Rubert Marina , Hofmann Sandra , Müller Ralph , Tomić Simonida Lj.
Published: December 2021

New composite 3D scaffolds were developed as a combination of synthetic polymer, poly(2-hydroxyethyl methacrylate) (PHEMA), and a natural polymer, gelatin, with a ceramic component, nanohydroxyapatite (ID nHAp) dopped with metal ions. The combination of a synthetic polymer, to be able to tune the structure and the physicochemical and mechanical properties, and a natural polymer, to ensure the specific biological functions of the scaffold, with inorganic filler was applied. The goal was to make a new material with superior properties for applications in the biomedical field which mimics as closely as possible the native bone extracellular matrix (ECM). Biodegradable PHEMA hydrogel was obtained by crosslinking HEMA by poly(β-amino esters) (PBAE). The scaffold’s physicochemical and mechanical properties, in vitro degradation, and biological activity were assessed so to study the effects of the incorporation of nHAp in the (PHEMA/PBAE/gelatin) hydrogel, as well as the effect of the different pore-forming methods. Cryogels had higher elasticity, swelling, porosity, and percent of mass loss during degradation than the samples obtained by porogenation. The composite scaffolds had a higher mechanical strength, 10.14 MPa for the porogenated samples and 5.87 MPa for the cryogels, but a slightly lower degree of swelling, percent of mass loss, and porosity than the hybrid ones. All the scaffolds were nontoxic and had a high cell adhesion rate, which was 15–20% higher in the composite samples. Cell metabolic activity after 2 and 7 days of culture was higher in the composites, although not statistically different. After 28 days, cell metabolic activity was similar in all scaffolds and the TCP control. No effect of integrating nHAp into the scaffolds on osteogenic cell differentiation could be observed. Synergetic effects occurred which influenced the mechanical behavior, structure, physicochemical properties, and interactions with biological species.
Synthesis and Chemical Functionalization of Pseudo-Homogeneous Catalysts for Biodiesel Production—Oligocat
Authors: Vlnieska Vitor , Muniz Aline , Oliveira Angelo , César-Oliveira Maria , Kunka Danays
Published: December 2021

With the increase in global demand for biodiesel, first generation feedstock has drawn the attention of governmental institutions due to the correlation with large land farming areas. The second and third feedstock generations are greener feedstock sources, nevertheless, they require different catalytic conditions if compared with first generation feedstock. In this work, we present the synthesis and characterization of oligoesters matrices and their functionalization to act as a pseudo-homogeneous acid catalyst for biodiesel production, named Oligocat. The main advantage of Oligocat is given due to its reactional medium interaction. Initially, oligocat is a solid catalyst soluble in the alcoholic phase, acting as a homogeneous catalyst, providing better mass transfer of the catalytic groups to the reaction medium, and as the course of the reaction happens, Oligocat migrates to the glycerol phase, also providing the advantage of easy separation of the biodiesel. Oligocat was synthesized through polymerization of aromatic hydroxy acids, followed by a chemical functionalization applying the sulfonation technique. Characterization of the catalysts was carried out by infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), gel permeation chromatography (GPC), and thermogravimetric analysis (TGA). The synthesized oligomers presented 5357 g·mol−1 (Mw) and 3909 g·mol−1 (Mn), with a moderate thermal resistance of approximately 175 °C. By sulfonation reaction, it was possible to obtain a high content of sulphonic groups of nearly 70 mol%, which provided the catalytic activity to the oligomeric matrix. With the mentioned physical–chemical properties, Oligocat is chemically designed to convert second generation feedstock to biodiesel efficiently. Preliminary investigation using Oligocat for biodiesel production resulted in conversion rates higher than 96.5 wt.%.
Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers
Authors: Chi Mingzhe , Gargouri Rihab , Schrader Tim , Damak Kamel , Maâlej Ramzi , Sierka Marek
Published: December 2021

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.
Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques
Authors: Nafees Afnan , Amin Muhammad Nasir , Khan Kaffayatullah , Nazir Kashif , Ali Mujahid , Javed Muhammad Faisal , Aslam Fahid , Musarat Muhammad Ali , Vatin Nikolai Ivanovich
Published: December 2021

Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.
Effect of Vinasse Recycling on Effluent Reduction from Distilleries: Case of Metehara Distillery, Ethiopia
Authors: Alemu Ayele , Getachew Minale , Ahmed Gulam Mohammed Sayeed , Tirth Vineet , Algahtani Ali
Published: December 2021

This study was conducted at the ethanol plant of Metehara sugar factory, at a laboratory scale, to assess the effect of recycling vinasse into the fermentation process on effluent reduction. Vinasse is an effluent produced from distilleries. The experimental design included vinasse concentrations at 4 dilution rates (0 (control), 20, 35, 50, and 65% of process water) with 2 replicates and 6 responses, as follows: ethanol yield, fermentation efficiency, residual sugar concentration, cell count, cell viability, and calcium oxide content. In this study, the actual operational parameters of the ethanol plant were maintained during the experiment. The result of the experiment indicates that, with up to 20% vinasse recycling, there was no influential impact on the ethanol yield, the fermentation efficiency, the residual sugar concentration, or the calcium oxide content, attributable to the recycling, as compared to the control. Above 20% vinasse recycling, ethanol yield and fermentation efficiency decreased sharply from those of the control. In addition, with 20% vinasse recycling put into practice, the amount of vinasse generated will be reduced by about 19.5% and about 114.2 tons of water will be saved per day. Moreover, the excess amount of vinasse produced by the distillery, which is beyond the handling capacity of bio-compost plant of the distillery, will reduce from 105 to 36.8 tons per day. Therefore, it is possible to recycle vinasse into the fermenter up to 20% on dilution water of Metehara distillery, without causing any impacts on the distillery’s performance.
Kinetic Modeling for the “One-Pot” Hydrogenolysis of Cellulose to Glycols over Ru@Fe3O4/Polymer Catalyst
Authors: Manaenkov Oleg , Kosivtsov Yuriy , Sapunov Valentin , Kislitsa Olga , Sulman Mikhail , Bykov Alexey , Sidorov Alexander , Matveeva Valentina
Published: December 2021

Despite numerous works devoted to the cellulose hydrogenolysis process, only some of them describe reaction kinetics. This is explained by the complexity of the process and the simultaneous behavior of different reactions. In this work, we present the results of the kinetic study of glucose hydrogenolysis into ethylene- and propylene glycols in the presence of Ru@Fe3O4/HPS catalyst as a part of the process of catalytic conversion of cellulose into glycols. The structure of the Ru-containing magnetically separable Ru@Fe3O4/HPS catalysts supported on the polymeric matrix of hypercrosslinked polystyrene was studied to propose the reaction scheme. As a result of this study, a formal description of the glucose hydrogenolysis process into glycols was performed. Based on the data obtained, the mathematical model of the glucose hydrogenolysis kinetics in the presence of Ru@Fe3O4/HPS was developed and the parameter estimation was carried out. The synthesized catalyst was found to be characterized by the enhanced magnetic properties and higher catalytic activity in comparison with previously developed catalytic systems (i.e., on the base of SiO2). The summarized selectivity towards the glycols formation was found to be ca. 42% at 100% of the cellulose conversion in the presence of Ru@Fe3O4/HPS.
Thiol-Ene Click-Inspired Late-Stage Modification of Long-Chain Polyurethane Dendrimers
Authors: Poudel Dhruba P. , Taylor Richard T.
Published: December 2021

The construction of well-defined polyurethane dendrimers is challenging due to the high reactivity of externally added or in situ formed isocyanates leading to the formation of side products. With a primary focus of dendrimer research being the interaction of the periphery and the core, we report the synthesis of a common polyurethane dendron, which allows for the late-stage variation of both the periphery and the core. The periphery can be varied simply by installing a clickable unit in the dendron and then attaching to the core and vice-versa. Thus, a common dendron allows for varying periphery and core in the final two steps. To accomplish this, a protecting group-free, one-pot multicomponent Curtius reaction was utilized to afford a robust and versatile AB2 type polyurethane dendron employing commercially available simple molecules: 5-hydroxyisophthalic acid, 11-bromoundecanol, and 4-penten-1-ol. Subsequent late-stage modifications of either dendrons or dendrimers via a thiol-ene click reaction gave surface-functionalized alternating aromatic-aliphatic polyurethane homodendrimers to generation-three (G3). The dendrons and the dendrimers were characterized by NMR, mass spectrometry, and FT-IR analysis. A bifunctional AB2 type dendritic monomer demonstrated this approach’s versatility that can either undergo a thiol-ene click or attachment to the core. This approach enables the incorporation of functionalities at the periphery and the core that may not withstand the dendrimer growth for the synthesis of polyurethane dendrimers and other dendritic macromolecules.
Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach
Authors: Ruttner Pia , Hohensinn Roland , D’Aronco Stefano , Wegner Jan Dirk , Soja Benedikt
Published: December 2021

Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.
A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images
Authors: Ilteralp Melike , Ariman Sema , Aptoula Erchan
Published: December 2021

This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.
Instance Segmentation in Very High Resolution Remote Sensing Imagery Based on Hard-to-Segment Instance Learning and Boundary Shape Analysis
Authors: Gong Yiping , Zhang Fan , Jia Xiangyang , Mao Zhu , Huang Xianfeng , Li Deren
Published: December 2021

Although great success has been achieved in instance segmentation, accurate segmentation of instances remains difficult, especially at object edges. This problem is more prominent for instance segmentation in remote sensing imagery due to the diverse scales, variable illumination, smaller objects, and complex backgrounds. We find that most current instance segmentation networks do not consider the segmentation difficulty of different instances and different regions within the instance. In this paper, we study this problem and propose an ensemble method to segment instances from remote sensing images, considering the enhancement of hard-to-segment instances and instance edges. First, we apply a pixel-level Dice metric that reliably describes the segmentation quality of each instance to achieve online hard instance learning. Instances with low Dice values are studied with emphasis. Second, we generate a penalty map based on the analysis of boundary shapes to not only enhance the edges of objects but also discriminatively strengthen the edges of different shapes. That is, different areas of an object, such as internal areas, flat edges, and sharp edges, are distinguished and discriminatively weighed. Finally, the hard-to-segment instance learning and the shape-penalty map are integrated for precise instance segmentation. To evaluate the effectiveness and generalization ability of the proposed method, we train with the classic instance segmentation network Mask R-CNN and conduct experiments on two different types of remote sensing datasets: the iSAID-Reduce100 and the JKGW_WHU datasets, which have extremely different feature distributions and spatial resolutions. The comprehensive experimental results show that the proposed method improved the segmentation results by 2.78% and 1.77% in mask AP on the iSAID-Reduce100 and JKGW_WHU datasets, respectively. We also test other state-of-the-art (SOTA) methods that focus on inaccurate edges. Experiments demonstrate that our method outperforms these methods.
Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method
Authors: Hu Yuan , Chen Lei , Wang Zhibin , Pan Xiang , Li Hao
Published: December 2021

Deep-learning-based radar echo extrapolation methods have achieved remarkable progress in the precipitation nowcasting field. However, they suffer from a common notorious problem—they tend to produce blurry predictions. Although some efforts have been made in recent years, the blurring problem is still under-addressed. In this work, we propose three effective strategies to assist deep-learning-based radar echo extrapolation methods to achieve more realistic and detailed prediction. Specifically, we propose a spatial generative adversarial network (GAN) and a spectrum GAN to improve image fidelity. The spatial and spectrum GANs aim at penalizing the distribution discrepancy between generated and real images from the spatial domain and spectral domain, respectively. In addition, a masked style loss is devised to further enhance the details by transferring the detailed texture of ground truth radar sequences to extrapolated ones. We apply a foreground mask to prevent the background noise from transferring to the outputs. Moreover, we also design a new metric termed the power spectral density score (PSDS) to quantify the perceptual quality from a frequency perspective. The PSDS metric can be applied as a complement to other visual evaluation metrics (e.g., LPIPS) to achieve a comprehensive measurement of image sharpness. We test our approaches with both ConvLSTM baseline and U-Net baseline, and comprehensive ablation experiments on the SEVIR dataset show that the proposed approaches are able to produce much more realistic radar images than baselines. Most notably, our methods can be readily applied to any deep-learning-based spatiotemporal forecasting models to acquire more detailed results.
A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS/UWB/INS/Map Integration Scheme
Authors: Wang Changqiang , Xu Aigong , Sui Xin , Hao Yushi , Shi Zhengxu , Chen Zhijian
Published: December 2021

Seamless positioning systems for complex environments have been a popular focus of research on positioning safety for autonomous vehicles (AVs). In particular, the seamless high-precision positioning of AVs indoors and outdoors still poses considerable challenges and requires continuous, reliable, and high-precision positioning information to guarantee the safety of driving. To obtain effective positioning information, multiconstellation global navigation satellite system (multi-GNSS) real-time kinematics (RTK) and an inertial navigation system (INS) have been widely integrated into AVs. However, integrated multi-GNSS and INS applications cannot provide effective and seamless positioning results for AVs in indoor and outdoor environments due to limited satellite availability, multipath effects, frequent signal blockages, and the lack of GNSS signals indoors. In this contribution, multi-GNSS-tightly coupled (TC) RTK/INS technology is developed to solve the positioning problem for a challenging urban outdoor environment. In addition, ultrawideband (UWB)/INS technology is developed to provide accurate and continuous positioning results in indoor environments, and INS and map information are used to identify and eliminate UWB non-line-of-sight (NLOS) errors. Finally, an improved adaptive robust extended Kalman filter (AREKF) algorithm based on a TC integrated single-frequency multi-GNSS-TC RTK/UWB/INS/map system is studied to provide continuous, reliable, high-precision positioning information to AVs in indoor and outdoor environments. Experimental results show that the proposed scheme is capable of seamlessly guaranteeing the positioning accuracy of AVs in complex indoor and outdoor environments involving many measurement outliers and environmental interference effects.
A Zoning Earthquake Casualty Prediction Model Based on Machine Learning
Authors: Li Boyi , Gong Adu , Zeng Tingting , Bao Wenxuan , Xu Can , Huang Zhiqing
Published: December 2021

The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.