bluuebunny/crossref_metadata_2025_split huggingface dataset:
Utilization of Dairy Scum Waste as a Feedstock for Biodiesel Production via Different Heating Sources for Catalytic Transesterification
Authors: Mohd Johari Siti Aminah , Ayoub Muhammad , Inayat Abrar , Ullah Sami , Uroos Maliha , Naqvi Salman Raza , Farukkh Sarah 
Published: July 2022

AbstractThe increasing demand of the world energy consumption has made it necessary to shift energy technologies toward renewable energy sources. The sustainability of feedstock can be maintained when utilizing feedstock from waste sources, such as dairy waste, food waste, and others. Dairy waste is one of the cheapest sources available. The compositions of dairy waste scum, including free fatty acids (8–10 %), triglycerides (more than 80 % in dry bases), and fats around 60 %, which have the potential to be used as the feedstock for biodiesel production in the presence of certain catalysts. To ensure more sustainability, the catalyst should be derived from waste (e.g., eggshells and cow bones) that consists of calcium oxide, which can then be used to catalyze the transesterification of dairy waste. This review emphasizes the current production of dairy waste globally and the potential of the waste and other types of organic waste as feedstock for biodiesel production, as well as determines the optimum reaction conditions for high‐quality biodiesel production.
Predicting single kernel and bulk milled rice alkali spreading value and gelatinization temperature class using NIR spectroscopy
Authors: Armstrong Paul R. , Maghirang Elizabeth B. , Chen Ming‐Hsuan , McClung Anna M. , Yaptenco Kevin F. , Brabec Daniel , Wu Tingting 
Published: July 2022

AbstractBackground and ObjectivesThe alkali spreading value (ASV) of rice is a widely measured quality parameter and accepted indicator of gelatinization temperature (GT) class. However, the alkali test, developed in 1958, is labor intensive and subjective. Better methods to measure ASV and GT, for single kernel and bulk rice would provide an important tool to determine the effects of individual kernels on end‐use quality and rice with desired cooking qualities. An instrument developed by the USDA‐ARS and a commercially available near infrared (NIR) instrument were evaluated for determining ASV and classification of intermediate and low GT levels for single kernel and bulk milled rice, respectively.FindingsQuantitative prediction of ASV scores (2–7) demonstrated the potential of NIR spectroscopy for screening with a standard error of prediction for validation samples ranging from 0.91 to 1.39 for the single kernel NIR instrument, and from 0.97 to 1.19 for the commercial instrument. GT categorization into intermediate and low values, using ASV scores, showed 82.4% and 85.0% correct classification using 1 and 30‐single kernel average calibration models, respectively. GT was correctly classified (93.6%–84.4%) using a commercial NIR instrument.ConclusionNIR spectroscopy has potential for rough screening of ASV and for two‐category GT rice classification.Significance and NoveltyConsidering that NIR spectroscopy has been proven to be applicable for other quality parameters, such as rice starch content and quality, protein content, and milling degree, the addition of calibrations for ASV as a predictor of GT class will be highly beneficial while not requiring additional resources. The rapid and nondestructive classification of individual kernels may also enable physical segregation of individual kernels for use by rice researchers and/or industry for additional studies on kernels with specific quality parameters and for determining the extent of variant kernels in a milled rice lot that could affect end use quality. Future studies on the use of NIR spectroscopy for brown rice should be evaluated so that quality could be assessed, while maintaining seed viability, and then used in field studies.
Implications of end‐of‐life home care compared with hospital‐based care during the COVID‐19 pandemic: A case report
Authors: Sakamoto Ryo , Bhandari Divya , Yoshida Makoto , Miyatake Hirotomo , Kosaka Makoto , Ozaki Akihiko , Tanimoto Tetsuya , Kami Masahiro 
Published: July 2022

AbstractRestriction on hospital visits for COVID‐19 infection control continues to have a significant negative impact on patients and their families. For a patient receiving palliative care, this social isolation may deteriorate their mental health. In such situations, home care could be a viable solution to this problem.
A case of post COVID‐19 subacute thyroiditis
Authors: Al‐Shammaa Mohamed Sadoon , Abdlkadir Ahmed Saad 
Published: July 2022

AbstractSubacute thyroiditis is a type of thyroid disease that occurs after a viral infection. This is usually a temporary condition associated with neck pain and may be accompanied by sore throat and flu‐like symptoms. We present a case of post COVID‐19 subacute thyroiditis and review its presentation and outcome.
COVID‐19‐associated pityriasis rosea in children: Case report and literature review
Authors: Khalili Maryam , Abtahi‐Naeini Bahareh , Rastegarnasab Fereshte , Afshar Kimia 
Published: July 2022

AbstractSkin lesions are one of the Coronavirus disease 2019 (COVID‐19) symptoms. Pityriasis rosea (PR) is a mucocutaneous manifestation that can occur following virus infections. Most of the PR lesions after COVID‐19 infection were reported in adults. Herein, we report a child with PR lesions, and a literature review on 5 other case reports in children.
Biocatalytic Intramolecular C−H aminations via Engineered Heme Proteins: Full Reaction Pathways and Axial Ligand Effects
Authors: Wei Yang , Conklin Melissa , Zhang Yong 
Published: July 2022

AbstractEngineered heme protein biocatalysts provide an efficient and sustainable approach to develop amine‐containing compounds through C−H amination. A quantum chemical study to reveal the complete heme catalyzed intramolecular C−H amination pathway and protein axial ligand effect was reported, using reactions of an experimentally used arylsulfonylazide with hemes containing L=none, SH−, MeO−, and MeOH to simulate no axial ligand, negatively charged Cys and Ser ligands, and a neutral ligand for comparison. Nitrene formation was found as the overall rate‐determining step (RDS) and the catalyst with Ser ligand has the best reactivity, consistent with experimental reports. Both RDS and non‐RDS (nitrene transfer) transition states follow the barrier trend of MeO−<SH−<MeOH<None due to the charge donation capability of the axial ligand to influence the key charge transfer process as the electronic driving forces. Results also provide new ideas for future biocatalyst design with enhanced reactivities.
Enantioseparation and antioxidant activity of novel diarylpyrazoline derivatives
Authors: Ameur Messaouda , Sekkoum Khaled , Gonazles Francisco , Comez‐Carpintero Jorge , Menendez Carlos , Belboukhari Nasser , Aboul‐Enein Hassan Y. 
Published: July 2022

AbstractThree chiral pyrazoline derivatives were synthesized by a flavanone ring‐opening reaction followed by cyclocondensation with hydrazine hydrate to give better yields. Their enantiomeric resolution was achieved using polysaccharide chiral stationary phase columns consisting of cellulose (Chiralcel®OD‐RH, Chiralcel®OZ‐3) and amylose (Chiralpak®IA) by high‐performance liquid chromatography. The separation was affected by the nature and concentration of the alcohol modifiers in the mobile phase. Taking 5‐methoxy‐2‐(3‐phenyl‐4,5‐dihydro‐1H‐pyrazol‐5‐yl)phenol (3a) as an example, the best separation was obtained by using the Chiralpak®IA column, with a separation factor α = 1.24 and Rs = 5.66 within an analysis time of <30 min. The diarylpyrazolines showed good antioxidant activity, studied by the DPPH method.
Ribisins and Certain Analogues Exert Neuroprotective Effects through Activation of the Keap1‐Nrf2‐ARE Pathway
Authors: Tan Shen H. , Karuppasamy Muthukumar , Lan Ping , Zhang Yaochun , Hu Jiayi , Lai Xingchen , Siok‐Cheng Lim Belinda , Liu Weiliang , Chen Jing , Chew Eng‐Hui , Banwell Martin G. 
Published: July 2022

AbstractOxidative stress contributes to the pathogenesis of various neurodegenerative diseases and induction of the Kelch‐like ECH‐associated protein 1 (Keap1)‐nuclear factor erythroid 2‐related factor 2 (Nrf2) pathway is a validated neuroprotective strategy. Synthetically‐derived samples of members of the ribisin class of natural product together with a range of analogues were evaluated for their neuroprotective capacities. Four of the twenty‐four compounds tested were found to strongly stimulate antioxidant response element‐dependent transcriptional activity in human‐derived SH‐SY5Y cells. Further, in rat pheochromocytoma PC12 cells and mouse brain cortical cultures these compounds upregulated levels of nuclear factor erythroid 2‐related factor 2 (Nrf2) and its downstream target gene products, namely heme oxygenase (HO‐1) and NAD(P)H quinone reductase 1 (NQO1). Functionally speaking, the compounds conferred protection in these cell models challenged with H2O2. In silico molecular modeling suggests that certain of the ribisins can dock in the Nrf2‐binding Kelch domain in Keap1, while cysteine labeling by biotinylated iodoacetamide suggest that cysteine residues within Keap1 react with the ribisins. It is thus proposed that the most active compounds exert their neuroprotective activities by targeting Keap1, thereby activating Nrf2 and so increasing transactivation of Nrf2‐responsive genes that encode for detoxifying and antioxidant enzymes.
Wetland position in the landscape: Impact on water storage and flood buffering
Authors: Åhlén Imenne , Thorslund Josefin , Hambäck Peter , Destouni Georgia , Jarsjö Jerker 
Published: July 2022

AbstractOn‐going climatic changes and land‐use changes may impact water storage dynamics within wetlandscapes (defined as the entire hydrological catchments of interconnected wetland systems). These dynamics are closely linked to many wetland ecosystem services including flood buffering, nutrient retention and biodiversity support. Here, we investigate if and how current water storage dynamics can differ between wetlands within the same wetlandscape. Based on continuous monitoring of water levels in multiple wetlands throughout a growing season (spring, summer, autumn) in Vattholma, Sweden, we show that there are two distinct storage behaviours depending on the position of the wetland within the landscape. Headwater wetland regions were active in temporary storage of surplus water from regular summer rains while water levels of downstream wetlands dropped to seasonal low values without responding to individual summer precipitation events. Thereby, the downstream wetlands maintained capacity to buffer extreme floods. We also found that headwater wetlands were associated with complex and patchy inundation, which causes habitat conditions to vary over short time scales both within and among these wetlands, in contrast to the prolonged low‐water state of the downstream wetlands. These differences between headwater‐downstream wetlands imply that the functionality of an entire wetlandscape cannot be assessed by simple extrapolation of data from monitoring stations that typically are located downstream of headwater regions. Increased understanding of these differences can support wetland management practices that target location‐specific nature‐based solutions and ecosystem services.
Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing
Authors: Paul Ananya , Mitra Sulata 
Published: July 2022

AbstractIn the current era, the coordination of traffic flow is hindered by the discrepancy between road infrastructure and the number of vehicles which leads to traffic congestion. One of the widely used strategies to mitigate traffic congestion is to control traffic signals with the help of deep reinforcement learning (DRL) in edge computing based intelligent transportation system. This article provides a comprehensive analysis of the most recent DRL algorithms, advantage actor‐critic and proximal policy optimization in multiple deep neural networks (DNNs), including a state‐of‐the‐art transformer model for effective traffic signal management. Here, a single DRL agent is used, which obtains the spatio‐temporal information of the traffic to identify traffic patterns from complex intersection environments. The agent uses this information as the input to the DNNs and then applies the algorithms to retrieve the essential parameters of DNN to seek an optimal action selection policy to mitigate congestion. Different real‐time maps and small city networks are explored here to determine which DNN is best suited for traffic congestion management. The simulation study reveals that both the algorithms significantly outperform the baseline. The transformer model gives the best result when compared to other DNNs. The transformer model decreases average waiting time by 96.16%, implying that it has a higher capability of dealing with congested environments.
Hybrid error correction in fragmented IoT media streams
Authors: Herrero Rolando 
Published: July 2022

AbstractWireless personal area networks (WPANs), as most IoT networking technologies, rely on constrained low power devices that support a trade‐off between signal coverage and transmission rates. WPANs are characterized for medium to low transmission rates over low distances. The transmission rates, however, are high enough to support maximum transmission unit (MTU) sizes that are large enough to enable the transmission of IPv6 datagrams. One such WPAN technologies is associated with the IEEE 802.15.4 physical layer. Because IEEE 802.15.4 does not natively support IP encapsulation, an adaptation mechanism known as IPv6 over low‐power wireless personal area networks (6LoWPAN) is used to this end. For large datagrams like those generated when transmitting speech frames, 6LoWPAN fragments them in order to comply with the 127‐byte IEEE 802.15.4 MTU size. Fragmentation, however, aggravates the effects of network packet loss. This article explores this problem and attempts to find a mechanism that overcomes these limitations. Specifically, it introduces a hybrid error correction algorithm that dynamically adjusts the number of fragments that are transmitted in order to balance application layer loss and transmission rate.
Systematic evaluation of machine learning algorithms for neuroanatomically‐based age prediction in youth
Authors: Modabbernia Amirhossein , Whalley Heather C. , Glahn David C. , Thompson Paul M. , Kahn Rene S. , Frangou Sophia 
Published: July 2022

AbstractApplication of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain‐age). The choice of the ML approach in estimating brain‐age in youth is important because age‐related brain changes in this age‐group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5–22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9–10 years and another comprising 594 individuals aged 5–21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree‐based, and kernel‐based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross‐validation folds, number of extreme outliers, and sample size. Tree‐based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain‐age in youth.
Metamaterials: From fundamental physics to intelligent design
Authors: Chen Ji , Hu Shanshan , Zhu Shining , Li Tao 
Published: July 2022

AbstractMetamaterials are artificial structures with the ability to efficiently control light‐field, attracting intensive attention in the past few decades. People have studied the working principles, design strategies, and fabrication methods of metamaterials, making this field cross and combine with many disciplines, including physics, material science, electronics, and chemistry. In recent years, with the rapid development of high‐efficiency and multifunctional metasurfaces, which are a two‐dimensional version of metamaterials, great efforts have been made to push this material to practical applications. In particular, the introduction of artificial intelligent (AI) algorithms enables metamaterials‐based photonic devices that exhibit excellent performances and intelligent functionalities. In this review, we first introduce the basic concepts, working principles, design methods, and applications of metamaterials, and then focus on the rapidly developing metamaterials research combined with AI algorithms. Finally, we conclude this review with personal perspectives on the current problems and future directions of metamaterials research and developments.
ABO blood group and COVID‐19 in pregnant women: A case–control study
Authors: Torres Alarcón Carmen Gabriela , Cruz Gonzalez Omar David , Sandoval López Chantal , Martinez Alvarez Roberto 
Published: July 2022

SynopsisStudy comparing ABO blood group distribution between pregnant women with or without COVID‐19. Blood group ABO did not affect COVID‐19 susceptibility, severity, or death.
Influence of the calcium orthophosphate:glass ratio and calcium orthophosphate functionalization on the degree of conversion and mechanical properties of resin‐based composites
Authors: Campos Amanda Lopes , Fronza Bruna Marin , Rodrigues Marcela Charantola , Souza Chiari Marina Damasceno e , Braga Roberto Ruggiero 
Published: July 2022

AbstractThe study verified the influence of calcium orthophosphate (CaP):glass ratio on the degree of conversion and mechanical properties of resin‐based composites containing either TEGDMA‐functionalized dicalcium phosphate anhydrous (DCPA) or non‐functionalized DCPA particles. The null hypotheses were that the evaluated variables are not affected by (1) CaP:glass ratio or (2) DCPA functionalization. DCPA particles were synthesized and half of them were functionalized with TEGDMA. Particle characterization included x‐ray diffraction, elemental analysis, laser scattering, helium picnometry and scanning electron microscopy. Two series of composites were prepared containing either DCPA‐NF (non‐functionalized) or DCPA‐F (functionalized), with total inorganic content of 50 vol % and DCPA:silanized barium glass (BG) ratios from 10:40 to 50:0. A composite containing 50 vol % BG was tested as control. DC was determined using FTIR spectroscopy. Biaxial flexural strength and modulus were tested after 24 h in water. Data were analyzed using Kruskal–Wallis/Dunn (flexural properties) or analysis of variance/Tukey tests (DC). Materials with similar actual DCPA contents were compared using Student's t test (alpha: 0.05). DC was higher for materials with DCPA‐F, except for the 10:40 ratio. DCPA‐F resulted in higher strength than DCPA‐NF only at 40:10 ratio. Modulus was not affected by functionalization. Materials with similar actual DCPA contents showed differences in DC (F > NF), while no difference in flexural properties was observed between materials with 28%–30% DCPA. Both null hypotheses were rejected.
Synthesis and biological evaluation of baloxavir marboxil analogs for the treatment of influenza A (H1N1) virus
Authors: Liu Yunlong , Wang Xueyuan , Lei Meng , Jiang Le , Miao Hang , Jiang Wanlin , Li Junwei , Zhu Yongqiang 
Published: July 2022

AbstractThe RNA‐dependent RNA polymerase (RdRp) of influenza virus, consisting of subunits of PA, PB1, and PB2 proteins, is responsible for viral mRNA transcription and vRNA replication. Cap‐dependent endonuclease (CEN) exists in PA subunit of RdRp and mediates the critical “cap‐snatching” step of viral RNA transcription, which is considered as a promising anti‐influenza target. In this manuscript, a series of novel compounds were designed, synthesized, and biologically investigated as CEN inhibitors. The cellular activities of the target compounds against influenza A/H1N1 virus were investigated and the structure–activity relationship (SAR) was summarized. The results showed that some compounds were active against influenza A/H1N1 virus with IC50 values of less than 100 μM. The microsomal metabolic stabilities were carried out in human, monkey, and rat species, and the results showed that the active ingredients of compounds 32 and 40 were stable enough to be in vivo investigated. The in vivo pharmacokinetic results showed that compound 32 had acceptable biological parameters for both ig and iv administrations. Then the in vivo toxicity study of compound 32 with the doses of 100 and 50 mg/kg BID was performed in BALB/C mice. No obvious toxicity of compound 32 was observed during the experiment. Finally, the in vivo efficacy study of compound 32 in BALB/C mice showed that it was effective in alleviating influenza symptoms and showed a significant pharmacodynamic dose dependence. These results indicated that compound 32 was a promising candidate for further development.
Green and highly efficient synthesis of pyrimidine derivatives in a novel glycolic acid: urea‐based low transition temperature mixture via C–C and C–O bond formation
Authors: Karande Vishram , Mohire Priyanka , Deshmukh Shubham , Patravale Ajinkya , Desai Vikram , Chandam Dattatray , Sankpal Sandeep , Deshmukh Madhukar 
Published: July 2022

AbstractThe present works describe a highly efficient synthesis of pyrimidine derivatives through a Knoevenagel condensation followed by Michael addition and subsequent cyclization using novel designer Low Transition Temperature Mixture (LTTM) in Glycolic acid: Urea (1:1). The mixture afforded as a green solvent which plays a dual role as a reusable catalyst and solvent, simple to prepare and nontoxic. The thermal stability and working temperature range of low‐temperature mixture were identified by Thermogravimetric analysis and Differential Scanning Calorimetry. The pyrimidine derivatives synthesized via a one‐pot three‐component reaction of two moles of Barbituric/Thiobarbituric acid and one mole of aryl aldehydes reacted through Knoevenagel condensation followed by Michael addition and subsequent cyclization. The Knoevenagel intermediate formed at room temperate while at 80°C Knoevenagel intermediate further reacted with another mole of barbituric acid/Thiobarbituric acid and formed the cyclic compounds. The use of LTTM to yield vital pyrimidine molecules renders this protocol to cope with the current need for an efficient, cost‐effective, and eco‐friendly methodology.