bluuebunny/crossref_metadata_2025 huggingface dataset:
A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy
Authors: Liang Chengji , Zhang Yue , Dong Liang
Published: December 2022

With the trend of terminal automation and the requirement for port operation efficiency to be greatly improved, it is very necessary to optimize the traveling route of automatic guided vehicles (AGV) with reference to the connection of loading and unloading equipment. As a complex multi-equipment system, it is inevitable that AGV will collide when traveling due to various accidents in actual operation, which will lead to AGV locking and reduce the efficiency of terminal operation. Considering the locking problem of AGV, we propose a three-stage integrated scheduling algorithm for AGV route planning. Through joint optimization with quay cranes (QC) and yard blocks, a road network model is established in the front area of the container port to optimize the path of AGV in the road network, and a speed control strategy is proposed to solve the problem of AGV collision avoidance. In the first stage, we establish the AGV optimal route model with the goal of minimizing the AGV path according to the AGV road network situation. In the second stage, on the basis of the determination of AGV route planning, and when the container task is known, the AGV task assignment model is established with the goal of minimizing the maximum completion time, and the model is solved by genetic algorithm (GA). In the third stage, on the basis of AGV task assignment and route determination, the AGV route and AGV task assignment scheme are input into the simulation model by establishing the AGV collision avoidance control model for speed control, and establishing the AGV route network simulation model for automated terminals considering collision avoidance in plant simulation software. The maximum completion time obtained from the simulation model is compared with the completion time obtained from the genetic algorithm. The proposed three-stage joint scheduling algorithm can improve the loading and unloading efficiency of the port, reduce the AGV locking situation, and has a certain contribution to the formulation of the actual operation planning of the port.
Practical Evaluation of Lyndon Factors via Alphabet Reordering
Authors: Albertini Marcelo K. , Louza Felipe A.
Published: December 2022

We evaluate the influence of different alphabet orderings on the Lyndon factorization of a string. Experiments with Pizza&Chili datasets show that for most alphabet reorderings, the number of Lyndon factors is usually small, and the length of the longest Lyndon factor can be as large as the input string, which is unfavorable for algorithms and indexes that depend on the number of Lyndon factors. We present results with randomized alphabet permutations that can be used as a baseline to assess the effectiveness of heuristics and methods designed to modify the Lyndon factorization of a string via alphabet reordering.
A New Mathematical Model of COVID-19 with Quarantine and Vaccination
Authors: Haq Ihtisham Ul , Ullah Numan , Ali Nigar , Nisar Kottakkaran Sooppy
Published: December 2022

A mathematical model revealing the transmission mechanism of COVID-19 is produced and theoretically examined, which has helped us address the disease dynamics and treatment measures, such as vaccination for susceptible patients. The mathematical model containing the whole population was partitioned into six different compartments, represented by the SVEIQR model. Important properties of the model, such as the nonnegativity of solutions and their boundedness, are established. Furthermore, we calculated the basic reproduction number, which is an important parameter in infection models. The disease-free equilibrium solution of the model was determined to be locally and globally asymptotically stable. When the basic reproduction number R0 is less than one, the disease-free equilibrium point is locally asymptotically stable. To discover the approximative solution to the model, a general numerical approach based on the Haar collocation technique was developed. Using some real data, the sensitivity analysis of R0 was shown. We simulated the approximate results for various values of the quarantine and vaccination populations using Matlab to show the transmission dynamics of the Coronavirus-19 disease through graphs. The validation of the results by the Simulink software and numerical methods shows that our model and adopted methodology are appropriate and accurate and could be used for further predictions for COVID-19.
Practical Exponential Stability of Impulsive Stochastic Food Chain System with Time-Varying Delays
Authors: Zhao Yuxiao , Wang Linshan
Published: December 2022

This paper studies the practical exponential stability of an impulsive stochastic food chain system with time-varying delays (ISOFCSs). By constructing an auxiliary system equivalent to the original system and comparison theorem, the existence of global positive solutions to the suggested system is discussed. Moreover, we investigate the sufficient conditions for the exponential stability and practical exponential stability of the system, which is given by Razumikhin technique and the Lyapunov method. In addition, when Razumikhin’s condition holds, the exponential stability and practical exponential stability of species are independent of time delay. Finally, numerical simulation finds the validity of the method.
Design of Observer and Dynamic Output Feedback Control for Fuzzy Networked Systems
Authors: Yao Hejun , Gao Fangzheng
Published: December 2022

The observer design and dynamic output feedback control for a class of nonlinear networked systems are studied in this paper. The model of the networked systems is established by using T-S fuzzy method, and the state observer of the systems is designed when the states of the systems are unknown. On this basis, the sufficient conditions for the exponential stability of the system are explored by using the linear matrix inequality (LMI) method and Lyapunov stability theory. Then, the dynamic output feedback control of the systems is designed by using the observer states, which ensures that the states of the closed-loop systems and the error systems exponentially converge to the origin at the same time. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the design method.
Evaluation of Machine Learning-Based Parsimonious Models for Static Modeling of Fluidic Muscles in Compliant Mechanisms
Authors: Trojanová Monika , Hošovský Alexander , Čakurda Tomáš
Published: December 2022

This paper uses computational intelligence and machine learning methods to describe experimental modeling performed to approximate the static characteristics of one type of fluidic muscle from the manufacturer FESTO for three different muscle sizes. For the experiments, measured data from the manufacturer and data from a real system (i.e., test device) were used. The measurements, which took place on the experimental equipment, were carried out in two stages (i.e., when the muscle was pressed and when the muscle was relaxed). The resulting measured characteristics were obtained by averaging two values at a given moment. MATLAB® software was used for simulations, in which four models were created: MLP, SVM, ANFIS, and a custom model (i.e., polynomial model). Given that most articles mainly interpret their results graphically when approximating characteristics, in this article, the outputs of the models are also compared with the measured data based on the SSE, NRMSE, SBC, and AIC performance indicators, enabling a more relevant and comprehensive overview of the performance of the individual models. The outputs of the best models described in this article reach an accuracy of 89.90% to 98.74% (all from the MLP group), depending on the muscle size, compared to real measured outputs.
Impacts of Casson Model on Hybrid Nanofluid Flow over a Moving Thin Needle with Dufour and Soret and Thermal Radiation Effects
Authors: Reddy Vinodh Srinivasa , Kandasamy Jagan , Sivanandam Sivasankaran
Published: December 2022

The current study used a novel Casson model to investigate hybrid Al2O3-Cu/Ethylene glycol nanofluid flow over a moving thin needle under MHD, Dufour–Soret effects, and thermal radiation. By utilizing the appropriate transformations, the governing partial differential equations are transformed into ordinary differential equations. The transformed ordinary differential equations are solved analytically using HAM. Furthermore, we discuss velocity profiles, temperature profiles, and concentration profiles for various values of governing parameters. Skin friction coefficient increases by upto 45% as the Casson parameter raised upto 20%, and the heat transfer rate also increases with the inclusion of nanoparticles. Additionally, local skin friction, a local Nusselt number, and a local Sherwood number for many parameters are entangled in this article.
The Role of Extracellular Vesicles in Diseases of the Ear, Nose, and Throat
Authors: Carnino Jonathan M , Miyawaki Steven , Rampam Sanjeev
Published: December 2022

Extracellular vesicles (EVs) are membranous nanoparticles produced by most cell types into the extracellular space and play an important role in cell-to-cell communication. Historically, EVs were categorized based on their methods of biogenesis and size into three groups: exosomes, microvesicles, and apoptotic bodies. Most recently, EV nomenclature has evolved to categorize these nanoparticles based on their size, surface markers, and/or the cell type which secreted them. Many techniques have been adopted in recent years which leverage these characteristics to isolate them from cell culture media and biological fluids. EVs carry various “cargo”, including DNA, RNA, proteins, and small signaling molecules. After isolation, EVs can be characterized by various methods to analyze their unique cargo profiles which define their role in cell-to-cell communication, normal physiology, and disease progression. The study of EV cargo has become more common recently as we continue to delineate their role in various human diseases. Further understanding these mechanisms may allow for the future use of EVs as novel biomarkers and therapeutic targets in diseases. Furthermore, their unique cargo delivery mechanisms may one day be exploited to selectively deliver therapeutic agents and drugs. Despite the growing research interest in EVs, limited studies have focused on the role of EVs in the diseases of the ear, nose, and throat. In this review, we will introduce EVs and their cargo, discuss methods of isolation and characterization, and summarize the most up-to-date literature thus far into the role of EVs in diseases of the ear, nose, and throat.
On the Determination of Elastic Properties of Indium Nitride Nanosheets and Nanotubes by Numerical Simulation
Authors: Sakharova Nataliya A. , Pereira André F. G. , Antunes Jorge M. , Chaparro Bruno M. , Fernandes José V.
Published: December 2022

Among the semiconductors formed by a 13th group element and nitrogen, indium nitride (InN) has promising electronic and optical properties, which make it an appropriate material for light-emitting devices and high-speed electronic applications. One-dimensional and two-dimensional InN structures, such as nanotubes and nanosheets, respectively, are expected to present novel advanced characteristics different from those of bulk InN, bringing new prospects in the designs of electronic and optical nanodevices. Despite the difficulties in the synthesis and mass production of the indium nitride nanotubes and nanosheets, the understanding of their properties, including mechanical ones, deserves more research attention, taking into account future perspectives. In this context, the present work is an exploratory study on the numerical evaluation of elastic properties of InN nanosheets and nanotubes, using the nanoscale continuum modelling (also called molecular structural mechanics) approach. The results obtained constitute a solid base for further investigation on the mechanical behaviour of the InN nanostructures, where studies are at an early stage or almost absent.
Applications of Nanoporous Gold in Therapy, Drug Delivery, and Diagnostics
Authors: Sondhi Palak , Lingden Dhanbir , Bhattarai Jay K. , Demchenko Alexei V. , Stine Keith J.
Published: December 2022

Nanoporous gold (np-Au) has promising applications in therapeutic delivery. The promises arise from its high surface area-to-volume ratio, ease of tuning shape and size, ability to be modified by organic molecules including drugs, and biocompatibility. Furthermore, np-Au nanostructures can generate the photothermal effect. This effect can be used either for controlled release of drugs of therapeutic importance or for destroying cancer cells by heating locally. Despite the enormous potential, the research on the therapeutical use of the np-Au is still in its early stage. In this review, we discuss the current progress and future directions of np-Au for therapeutic applications.
Traumatic Brain Injury Triggers Neurodegeneration in a Mildly Symptomatic MELAS Patient: Implications on the Detrimental Role of Damaged Mitochondria in Determining Head Trauma Sequalae in the General Population
Authors: Zanotti Simona , Velardo Daniele , Sciacco Monica
Published: December 2022

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome is a maternally inherited genetic mitochondrial disease with a typical onset in the first two decades of life and a major involvement of central nervous system (CNS). We present the case of a man affected with an oligosymptomatic, genetically determined MELAS syndrome, whose clinical picture dramatically and irreversibly worsened following a mild head injury. We hypothesize that the CNS metabolic stress induced by the brain injury activated an irreversible cascade of events leading to progressive neurodegeneration because damaged mitochondria were unable to restore the balance between energy requirements and availability.
Effect of Esomeprazole Treatment on Extracellular Tumor pH in a Preclinical Model of Prostate Cancer by MRI-CEST Tumor pH Imaging
Authors: Irrera Pietro , Roberto Miriam , Consolino Lorena , Anemone Annasofia , Villano Daisy , Navarro-Tableros Victor , Carella Antonella , Dastrù Walter , Aime Silvio , Longo Dario Livio
Published: December 2022

Novel anticancer treatments target the pH regulating system that plays a major role in tumor progression by creating an acidic microenvironment, although few studies have addressed their effect on tumor acidosis. In this study, we investigated in vivo several proton pump inhibitors (PPIs) targeting NHE-1 (Amiloride and Cariporide) and V-ATPase (Esomeprazole and Lansoprazole) proton transporters in the DU145 androgen-insensitive human prostate cancer model. In cellulo results showed that DU145 are sensitive, with decreasing efficacy, to Amiloride, Esomeprazole and Lansoprazole, with marked cell toxicity both in normoxia and in hypoxia, with almost any change in pH. In vivo studies were performed upon administration of Esomeprazole to assess both the acute and chronic effects, and Iopamidol-based tumor pH imaging was performed to evaluate tumor acidosis. Although statistically significant tumor pH changes were observed a few hours after Esomeprazole administration in both the acute study and up to one week of treatment in the chronic study, longer treatment resulted in a lack of changes in tumor acidosis, which was associated to similar tumor growth curves between treated and control groups in both the subcutaneous and orthotopic models. Overall, this study highlights MRI-CEST tumor pH imaging as a valid approach to monitoring treatment response to PPIs.
Design and Optimization of an S-Band MEMS Bandpass Filter Based on Aggressive Space Mapping
Authors: Wu Qiannan , Gao Xudong , Shi Zemin , Li Jing , Li Mengwei
Published: December 2022

Aggressive space mapping (ASM) is a common filter simulation and debugging method. It plays an important role in the field of microwave device design. This paper introduces ASM and presents the design and fabrication of a compact fifth-order microstrip interdigital filter with a center frequency of 2.5 GHz and a relative bandwidth of 10% using ASM. The filter used a double-layer silicon substrate structure and stepped impedance resonators (SIRs) and was optimized by ASM. After five iterations, the filter achieved the design specification, which greatly improves the efficiency of the filter design compared with the traditional method. It was fabricated on high-resistance silicon wafers by micro-electro-mechanical systems (MEMSs) technology, and the final size of the chip is 9.5 mm × 7.6 mm × 0.8 mm. The measurement results show that the characteristics of the filter are similar to the simulation results, which also shows the efficiency and precision of the ASM algorithm.
A Novel Integrated Transdermal Drug Delivery System with Micropump and Microneedle Made from Polymers
Authors: Attiguppe Ajay Prabhakar , Chatterjee Dhiman , DasGupta Amitava
Published: December 2022

Transdermal drug delivery (TDD), which enables targeted delivery with microdosing possibilities, has seen much progress in the past few years. This allows medical professionals to create bespoke treatment regimens and improve drug adherence through real-time monitoring. TDD also increases the effectiveness of the drugs in much smaller quantities. The use of polymers in the drug delivery field is on the rise owing to their low cost, scalability and ease of manufacture along with drug and bio-compatibility. In this work, we present the design, development and characterization of a polymer-based TDD platform fabricated using additive manufacturing technologies. The system consists of a polymer based micropump integrated with a drug reservoir fabricated by 3D printing and a polymer hollow microneedle array fabricated using photolithography. To the best of our knowledge, we present the fabrication and characterization of a 3D-printed piezoelectrically actuated non-planar valveless micropump and reservoir integrated with a polymer hollow microneedle array for the first time. The integrated system is capable of delivering water at a maximum flow rate of 1.03 mL/min and shows a maximum backpressure of 1.37 kPa while consuming only 400 mW. The system has the least number of moving parts. It can be easily fabricated using additive manufacturing technologies, and it is found to be suitable for drug delivery applications.
On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation
Authors: Zhang Lulu , Zhang Yongzhi , Liu Furong , Chen Qingyuan , Lian Yangbo , Ma Quanlong
Published: December 2022

Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200–2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.
Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures
Authors: Zhang Long , Bibi Farzana , Hussain Imtiyaz , Sultan Muhammad , Arshad Adeel , Hasnain Saqib , Alarifi Ibrahim M. , Alamir Mohammed A. , Sajjad Uzair
Published: December 2022

Extensive amount of research on additively manufactured (AM) lattice structures has been made to develop a generalized model that can interpret how strongly operational variables affect mechanical properties. However, the currently used techniques such as physics models and multi-physics simulations provide a specific interpretation of those qualities, and are not general enough to assess the mechanical properties of AM lattice structures of different topologies produced on different materials via several fabrication methods. To tackle this problem, this study develops an optimal deep learning (DL) model based on more than 4000 data points, which has been optimized by analyzing three different hyper-parameters optimization schemes including gradient boost regression trees (GBRT), gaussian process (GP), and random forest (RF) with different data distribution schemes such as normal distribution, nth root transformation, and robust scaler. With the robust scaler and nth root transformation, the accuracy of the model increases from R2 = 0.85 (for simple distribution) to R2 = 0.94 and R2 = 0.88, respectively. After feature engineering and data correlation, the stress, unit cell size, total height, width, and relative density are chosen to be the input parameters to model the strain. The optimal DL model is able to predict the strain of different topologies of lattices (such as circular, octagonal, Gyroid, truncated cube, Truncated cuboctahedron, Rhombic do-decahedron, and many others) with decent accuracy (R2 = 0.936, MAE = 0.05, and MSE = 0.025). The parametric sensitivity analysis and explainable artificial intelligence (by using DeepSHAP library) based insights confirm that stress is the most sensitive input to the strain followed by the relative density from the modeling perspective of the AM lattices. The findings of this study would be helpful for the industry and the researchers to design AM lattice structures of different topologies for various engineering applications.
Pyuria Is Associated with Dysbiosis of the Urinary Microbiota in Type 2 Diabetes Patients Receiving Sodium–Glucose Cotransporter 2 Inhibitors
Authors: Lin Hsueh-Ju , Chuang Han-Ni , Jhan Pei-Pei , Ye Han-Yu , Lee I-Te , Hsiao Tzu-Hung , Liu Po-Yu
Published: December 2022

Treating type 2 diabetes (T2D) patients with sodium–glucose cotransporter 2 (SGLT2) inhibitors may be associated with an increased risk of urinary tract infections (UTIs), such as diabetes-induced asymptomatic bacteriuria. Pyuria—a condition wherein leukocytes are detected in the urine—is a predictor of UTIs. The aim of this study was to examine the urinary microbiome of Taiwanese T2D patients, with or without pyuria, undergoing SGLT2 treatment. We recruited seven T2D patients, recorded their clinical and biochemical characteristics, and collected their urine samples for 16S metagenomic sequencing. The primary outcomes were the diversity of urinary microbiota and the relative abundance of different species. We found that the microbiome of the pyuria group was significantly less diverse than the non-pyuria group (0.24 ± 0.04 vs. 2.21 ± 0.28, p = 0.002), while the number of operational taxonomic units did not differ significantly (763.5 ± 78.67 and 747 ± 141.3, p = 0.92). Escherichia-Shigella spp. dominated the microbiome of the pyuria group (97.4%–99.4%), and these patients tended to have more comorbidities. In conclusion, pyuria is associated with urinary microbiota dysbiosis in T2D patients being treated with SGLT2 inhibitors.
Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards
Authors: Velasquez-Camacho Luisa , Otero Marta , Basile Boris , Pijuan Josep , Corrado Giandomenico
Published: December 2022

Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized farming system characterized by high crop protection cost and increasing adoption of precision agriculture techniques. Different methodological approaches, from traditional mechanistic or statistic models to machine and deep learning, are outlined with their main features, potential, and constraints. Our analysis indicated that strategies are being continuously developed to achieve the required goals of ease of monitoring and timely prediction of diseases. We also discuss that scientific and technological advances (e.g., in weather data, omics, digital solutions, sensing devices, data science) still need to be fully harnessed, not only for modelling plant–pathogen interaction but also to develop novel, integrated, and robust predictive systems and related applied technologies. We conclude by identifying key challenges and perspectives for predictive modelling of phytopathogenic disease in vineyards.
Concept about the Virulence Factor of Legionella
Authors: Yang Jin-Lei , Li Danyang , Zhan Xiao-Yong
Published: December 2022

Pathogenic species of Legionella can infect human alveolar macrophages through Legionella-containing aerosols to cause a disease called Legionellosis, which has two forms: a flu-like Pontiac fever and severe pneumonia named Legionnaires’ disease (LD). Legionella is an opportunistic pathogen that frequently presents in aquatic environments as a biofilm or protozoa parasite. Long-term interaction and extensive co-evolution with various genera of amoebae render Legionellae pathogenic to infect humans and also generate virulence differentiation and heterogeneity. Conventionally, the proteins involved in initiating replication processes and human macrophage infections have been regarded as virulence factors and linked to pathogenicity. However, because some of the virulence factors are associated with the infection of protozoa and macrophages, it would be more accurate to classify them as survival factors rather than virulence factors. Given that the molecular basis of virulence variations among non-pathogenic, pathogenic, and highly pathogenic Legionella has not yet been elaborated from the perspective of virulence factors, a comprehensive explanation of how Legionella infects its natural hosts, protozoans, and accidental hosts, humans is essential to show a novel concept regarding the virulence factor of Legionella. In this review, we overviewed the pathogenic development of Legionella from protozoa, the function of conventional virulence factors in the infections of protozoa and macrophages, the host’s innate immune system, and factors involved in regulating the host immune response, before discussing a probably new definition for the virulence factors of Legionella.
Phytoplankton Community Response to Environmental Factors along a Salinity Gradient in a Seagoing River, Tianjin, China
Authors: Sun Xuewei , Zhang Huayong , Wang Zhongyu , Huang Tousheng , Huang Hai
Published: December 2022

A river-estuary ecosystem usually features a distinct salinity gradient and a complex water environment, so it is enormously valuable to study the response mechanism of living organisms to multiple abiotic factors under salinity stress. Phytoplankton, as an important part of aquatic microorganisms, has always been of concern for its crucial place in the aquatic ecosystem. In this study, phytoplankton data and 18 abiotic factors collected from 15 stations in Duliujian River, a seagoing river, were investigated in different seasons. The results showed that the river studied was of a Cyanophyta-dominant type. Salinity (SAL) was the key control factor for phytoplankton species richness, while water temperature (WT) was critical not only for species richness, but also community diversity, and the abundance and biomass of dominant species. Apart from WT, the abundance and biomass of dominant species were also driven by total nitrogen (TN), nitrate (NO3−), pH, and water transparency (SD). Moreover, total dissolved phosphorus (TDP), pH, and chemical oxygen demand (COD) were crucial for community diversity and evenness. The bloom of dominant species positively associated with TDP led to lower diversity and evenness in autumn. In addition, when available nitrogen was limited, Pseudoanabaena sp. could obtain a competitive advantage through the N2 fixation function. Increased available nitrogen concentration could favor the abundance of Chlorella vulgaris to resist the negative effect of WT. The results show that Oscillatoria limosa could serve as an indicator of organic contamination, and nutrient-concentration control must be effective to inhibit Microcystis bloom. This could help managers to formulate conservation measures.
Atlantic Salmon (Salmo salar) Transfer to Seawater by Gradual Salinity Changes Exhibited an Increase in The Intestinal Microbial Abundance and Richness
Authors: Morales-Rivera María F. , Valenzuela-Miranda Diego , Nuñez-Acuña Gustavo , Benavente Bárbara P. , Gallardo-Escárate Cristian , Valenzuela-Muñoz Valentina
Published: December 2022

The host’s physiological history and environment determine the microbiome structure. In that sense, the strategy used for the salmon transfer to seawater after parr-smolt transformation may influence the Atlantic salmon’s intestinal microbiota. Therefore, this study aimed to explore the diversity and abundance of the Atlantic salmon intestinal microbiota and metagenome functional prediction during seawater transfer under three treatments. One group was exposed to gradual salinity change (GSC), the other to salinity shock (SS), and the third was fed with a functional diet (FD) before the seawater (SW) transfer. The microbial profile was assessed through full-16S rRNA gene sequencing using the Nanopore platform. In addition, metagenome functional prediction was performed using PICRUSt2. The results showed an influence of salinity changes on Atlantic salmon gut microbiota richness, diversity, and taxonomic composition. The findings reveal that GSC and the FD increased the Atlantic salmon smolt microbiota diversity, suggesting a positive association between the intestinal microbial community and fish health during seawater transfer. The reported knowledge can be applied to surveil the microbiome in smolt fish production, improving the performance of Atlantic salmon to seawater transfer.
Identification of a Phylogenetically Divergent Vanillate O-Demethylase from Rhodococcus ruber R1 Supporting Growth on Meta-Methoxylated Aromatic Acids
Authors: Donoso Raúl A. , Corbinaud Ricardo , Gárate-Castro Carla , Galaz Sandra , Pérez-Pantoja Danilo
Published: December 2022

Rieske-type two-component vanillate O-demethylases (VanODs) catalyze conversion of the lignin-derived monomer vanillate into protocatechuate in several bacterial species. Currently, VanODs have received attention because of the demand of effective lignin valorization technologies, since these enzymes own the potential to catalyze methoxy group demethylation of distinct lignin monomers. In this work, we identified a phylogenetically divergent VanOD from Rhodococcus ruber R1, only distantly related to previously described homologues and whose presence, along with a 3-hydroxybenzoate/gentisate pathway, correlated with the ability to grow on other meta-methoxylated aromatics, such as 3-methoxybenzoate and 5-methoxysalicylate. The complementation of catabolic abilities by heterologous expression in a host strain unable to grow on vanillate, and subsequent resting cell assays, suggest that the vanAB genes of R1 strain encode a proficient VanOD acting on different vanillate-like substrates; and also revealed that a methoxy group in the meta position and a carboxylic acid moiety in the aromatic ring are key for substrate recognition. Phylogenetic analysis of the oxygenase subunit of bacterial VanODs revealed three divergent groups constituted by homologues found in Proteobacteria (Type I), Actinobacteria (Type II), or Proteobacteria/Actinobacteria (Type III) in which the R1 VanOD is placed. These results suggest that VanOD from R1 strain, and its type III homologues, expand the range of methoxylated aromatics used as substrates by bacteria.
Mitigation of Marine Dinoflagellates Using Hydrogen Peroxide (H2O2) Increases Toxicity towards Epithelial Gill Cells
Authors: Mardones Jorge , Flores-Leñero Ana , Pinto-Torres Marco , Paredes-Mella Javier , Fuentes-Alburquenque Sebastián
Published: December 2022

Hydrogen peroxide (H2O2) has been shown to efficiently remove toxic microalgae from enclosed ballast waters and brackish lakes. In this study, in vitro experiments were conducted to assess the side effects of mitigating toxic and non-toxic dinoflagellates with H2O2. Five H2O2 concentrations (50 to 1000 ppm) were used to control the cell abundances of the toxic dinoflagellates Alexandrium catenella and Karenia selliformis and the non-toxic dinoflagellates Lepidodinium chlorophorum and Prorocentrum micans. Photosynthetic efficiency and staining dye measurements showed the high efficiency of H2O2 for mitigating all dinoflagellate species at only 50 ppm. In a bioassay carried out to test cytotoxicity using the cell line RTgill-W1, control experiments (only H2O2) showed cytotoxicity in a concentration- and time- (0 to 24 h) dependent manner. The toxic dinoflagellates, especially K. selliformis, showed basal cytotoxicity that increased with the application of hydrogen peroxide. Unexpectedly, the application of a low H2O2 concentration increased toxicity, even when mitigating non-toxic dinoflagellates. This study suggests that the fatty acid composition of toxic and non-toxic dinoflagellate species can yield toxic aldehyde cocktails after lipoperoxidation with H2O2 that can persist in water for days with different half-lives. Further studies are needed to understand the role of lipoperoxidation products as acute mediators of disease and death in aquatic environments.
Jarosite-Rich Mineral Crust on Coastal Cliffs in Central Norway: Microstructural and Geochemical Investigations
Authors: Johansen Stein Olle , Gebremariam Kidane Fanta
Published: December 2022

The study of jarosite produced under diverse conditions is essential to gain insight into its diverse formation mechanisms on earth. Such investigations can even pave ways to better understanding of the genesis of jarosite discovered in extra-terrestrial bodies such as Mars. Jarosite samples from two costal locations in central Norway are investigated through the application of multiple analytical techniques. The jarosite-rich encrustations on seaward cliff walls were studied with a focus on the characterization of their micromorphology and geochemistry. Light and electron microscopic analyses revealed distinct laminations and microlaminations in the samples. These layered laminations likely imply the existence of favorable periods in a cyclic manner for mineralization/biomineralization of jarosite in tandem with gypsum formation and dissolution. The pH level measured is not low similar to that usually described as conducive for jarosite formations. Different viable jarosite formation mechanisms are explored. Though some indicators are implied from microstructural and compositional analyses, further investigations are required for establishing the biogenic nature of the mechanism involved. Signs of the possible formation of jarosite in the Mid-Pleistocene Transition, 1.1–1.3 million years B.P., are acquired from Ar39/Ar40 geochronological determinations. Useful paleoenvironmental and paleobiological information could be found preserved in the microstructures of such jarosite formations.
Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan
Authors: Mohamed Taha Abdallah M. , Xi Yantao , He Qingping , Hu Anqi , Wang Shuangqiao , Liu Xianbin
Published: December 2022

Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.
Bioleaching of a Chalcocite-Dominant Copper Ore from Salta, Argentina, by Mesophilic and Thermophilic Microorganisms
Authors: Amar Agustina , Massello Francisco L. , Costa Cristina S. , Castro Camila , Donati Edgardo R.
Published: December 2022

The study and development of new sustainable mining methods to exploit low-grade ores and secondary metallic resources are essential to meet global demand and contribute to caring for the environment. Copper is one of the most widely used metals and chalcocite is the main secondary sulfide of this metal. Therefore, the study of copper recovery from chalcocite-dominant minerals could have a great impact on the industry. In this study, we assess at bench scale the feasibility of applying biohydrometallurgical processes to extract copper from chalcocite-rich minerals from Taca Taca, Argentina, using native mesophilic microorganisms (30 °C) and thermophiles (45, 65 °C). The indigenous mesophilic consortium was dominated by Acidithiobacillus ferrooxidans and could solubilize all the copper present in the systems (113 mg/L) within three weeks without any change in the pH of the solution. Notably, by increasing the temperature up to 45 and 65 °C, copper leaching was enhanced, completing the recovery in 7–14 days. The oxidizing microorganisms active in these conditions were Ferroplasma sp. and Acidianus copahuensis, respectively. An increase in the abiotic copper recovery was also observed as temperature rose; as well as a slight acidification of the solution. This study constitutes the first assessment for the bioleaching of Taca Taca ores.
High-Yield Characterization of Single Molecule Interactions with DeepTipTM Atomic Force Microscopy Probes
Authors: Corregidor Daniel , Tabraue Raquel , Colchero Luis , Daza Rafael , Elices Manuel , Guinea Gustavo V. , Pérez-Rigueiro José
Published: December 2022

Single molecule interactions between biotin and streptavidin were characterized with functionalized DeepTipTM probes and used as a model system to develop a comprehensive methodology for the high-yield identification and analysis of single molecular events. The procedure comprises the covalent binding of the target molecule to a surface and of the sensing molecule to the DeepTipTM probe, so that the interaction between both chemical species can be characterized by obtaining force–displacement curves in an atomic force microscope. It is shown that molecular resolution is consistently attained with a percentage of successful events higher than 90% of the total number of recorded curves, and a very low level of unspecific interactions. The combination of both features is a clear indication of the robustness and versatility of the proposed methodology.
Chemical Profiling and Nutritional Evaluation of Bee Pollen, Bee Bread, and Royal Jelly and Their Role in Functional Fermented Dairy Products
Authors: Darwish Amira , Abd El-Wahed Aida , Shehata Mohamed , El-Seedi Hesham , Masry Saad , Khalifa Shaden , Mahfouz Hatem , El-Sohaimy Sobhy
Published: December 2022

Honeybee products, as multicomponent substances, have been a focus of great interest. The present work aimed to perform the nutritional and chemical profiling and biochemical characterization of bee pollen (BP), bee bread (BB), and royal jelly (RJ) and study their applications in the fortification of functional fermented dairy products. Their effects on starter cultures and the physicochemical and sensorial quality of products were monitored. A molecular networking analysis identified a total of 46 compounds in the three bee products that could be potential medicines, including flavonoids, fatty acids, and peptides. BB showed the highest protein and sugar contents (22.57 and 26.78 g/100 g), which cover 45.14 and 53.56% of their daily values (DVs), with considerable amounts of the essential amino acids threonine and lysine (59.50 and 42.03%). BP, BB, and RJ can be considered sources of iron, as 100 g can cover 141, 198.5, and 94.94% of DV%, respectively. BP was revealed to have the highest phenolic and flavonoid contents (105.68 and 43.91 µg/g) and showed a synergetic effect when mixed with RJ, resulting in increased antioxidant activity, while BB showed a synergetic effect when mixed with RJ in terms of both antioxidant and proteolytic powers (IC50 7.54, 11.55, 12.15, 12.50, and 12.65 cP compared to the control (10.55 cP)), reflecting their organoleptic properties and highlighting these health-oriented products as promising natural products for human health care.
Dissecting Functional Biological Interactions Using Modular RNA Nanoparticles
Authors: Klotz Kaitlin , Radwan Yasmine , Chakrabarti Kausik
Published: December 2022

Nucleic acid nanoparticles (NANPs) are an exciting and innovative technology in the context of both basic and biomedical research. Made of DNA, RNA, or their chemical analogs, NANPs are programmed for carrying out specific functions within human cells. NANPs are at the forefront of preventing, detecting, and treating disease. Their nucleic acid composition lends them biocompatibility that provides their cargo with enhanced opportunity for coordinated delivery. Of course, the NANP system of targeting specific cells and tissues is not without its disadvantages. Accumulation of NANPs outside of the target tissue and the potential for off-target effects of NANP-mediated cargo delivery present challenges to research and medical professionals and these challenges must be effectively addressed to provide safe treatment to patients. Importantly, development of NANPs with regulated biological activities and immunorecognition becomes a promising route for developing versatile nucleic acid therapeutics. In a basic research context, NANPs can assist investigators in fine-tuning the structure-function relationship of final formulations and in this review, we explore the practical applications of NANPs in laboratory and clinical settings and discuss how we can use established nucleic acid research techniques to design effective NANPs.
Distribution and Stability of Polyphenols in Juices Made from Traditional Apple Cultivars Grown in Bosnia and Herzegovina
Authors: Oras Amila , Akagić Asima , Spaho Nermina , Gaši Fuad , Žuljević Sanja Oručević , Meland Mekjell
Published: December 2022

The present research was undertaken to investigate polyphenolic profiles of peel, pulp and juices made from two standard commercial and five traditional apple cultivars from Bosnia and Herzegovina. The main goal of the study was to monitor the distribution and changes of polyphenolic profiles through different phases of apples’ processing into cloudy and clear juices, with regard to L-ascorbic acid pretreatment. Quantitative determination of phenolic compounds was carried out by using high-performance liquid chromatography with diode-array detection. The obtained results showed that traditional cultivars, namely ‘Paradija’ and ‘Prijedorska zelenika’, displayed significantly higher content of these compounds compared to commercial ones. Flavan 3-ols and flavonol glycosides were mostly found in peels of all cultivars (21.2–44.1 and 5.40–33.3%, respectively), while phenolic acids along with flavan 3-ols were predominant in the pulp (8.20–30.8 and 5.10–13.9%, respectively). Apples’ processing into juices caused decrease (more than 90%) in the content of all polyphenols and the distribution of these compounds from fruits to final products had a negative trend, particularly evident in clear juices. The most drastic loss occurred in the flavonol glycosides and dihydrochalcones content, while chlorogenic acid displayed quite stable distribution from apples to final products due to its good solubility. Apple mash pretreatment with L-ascorbic acid had a positive impact on the preservation and retention of polyphenols.
In Situ Synthesis of C-N@NiFe2O4@MXene/Ni Nanocomposites for Efficient Electromagnetic Wave Absorption at an Ultralow Thickness Level
Authors: Sun Qing , Yang Xin , Shu Tie , Yang Xianfeng , Qiao Min , Wang Dashuang , Liu Zhaohui , Li Xinghua , Rao Jinsong , Zhang Yuxin , Yang Pingan , Yao Kexin
Published: December 2022

Recently, the development of composite materials composed of magnetic materials and MXene has attracted significant attention. However, the thickness and microwave absorption performance of the composite is still barely satisfactory. In this work, the C-N@NiFe2O4@MXene/Ni nanocomposites were successfully synthesized in situ by hydrothermal and calcination methods. Benefiting from the introduction of the carbon-nitrogen(C-N) network structure, the overall dielectric properties are improved effectively, consequently reducing the thickness of the composite while maintaining excellent absorption performance. As a result, the minimum reflection loss of C-N@NiFe2O4@MXene/Ni can reach −50.51 dB at 17.3 GHz at an ultralow thickness of 1.5 mm, with an effective absorption bandwidth of 4.95 GHz (13.02–18 GHz). This research provides a novel strategy for materials to maintain good absorption performance at an ultralow thickness level.
Nanostructured Lipid Carriers (NLC)-Based Gel Formulations as Etodolac Delivery: From Gel Preparation to Permeation Study
Authors: Czajkowska-Kośnik Anna , Szymańska Emilia , Winnicka Katarzyna
Published: December 2022

Topical administration of drug is an attractive alternative to the oral administration as it provides a reduction in adverse reactions and an enhancement of therapeutic effects. The use of lipid carriers in hydrogel structures makes it possible to introduce lipophilic substances in a dissolved form. In this study, an NSAID from the BCS class II, etodolac (ETD), was used. The nanostructured lipid carriers (NLC) obtained with ETD were incorporated into semi-solid forms (gels). Hydrogels with the suspended drug and oleogel were also prepared for comparison purposes. The obtained gels were tested in terms of pH, viscosity, rheological, mechanical, and bioadhesive properties. The release and permeation through membranes were also studied. All tested formulations were characterized by a pH below 7, which ensured the physiological state of the skin. The viscosities of all gels decreased with increasing shear rate, indicating non-Newtonian behavior. The fastest ETD release was observed for NLC with a Carbopol base (formulation F1); a similar result was noticed in the permeation test. The developed gel formulations containing ETD-NLC dispersion and Carbopol or Poloxamer as gelling agents were stable and possessed beneficial pharmaceutical properties.