bluuebunny/crossref_metadata_2025 huggingface dataset:
NSD1 Mutations and Pediatric High-Grade Gliomas: A Comparative Genomic Study in Primary and Recurrent Tumors
Authors: d’Amati Antonio , Nicolussi Arianna , Miele Evelina , Mastronuzzi Angela , Rossi Sabrina , Gianno Francesca , Buttarelli Francesca Romana , Minasi Simone , Lodeserto Pietro , Gardiman Marina Paola , Viscardi Elisabetta , Coppa Anna , Donofrio Vittoria , Giovannoni Isabella , Giangaspero Felice , Antonelli Manila
Published: December 2022

Pediatric high-grade gliomas represent a heterogeneous group of tumors with a wide variety of molecular features. We performed whole exome sequencing and methylation profiling on matched primary and recurrent tumors from four pediatric patients with hemispheric high-grade gliomas. Genetic analysis showed the presence of some variants shared between primary and recurrent tumors, along with other variants exclusive of primary or recurrent tumors. NSD1 variants, all novel and not previously reported, were present at high frequency in our series (100%) and were all shared between the samples, independently of primary or recurrence. For every variant, in silico prediction tools estimated a high probability of altering protein function. The novel NSD1 variant (c.5924T > A; p.Leu1975His) was present in one in four cases at recurrence, and in two in four cases at primary. The novel NSD1 variant (c.5993T > A; p.Met1998Lys) was present in one in four cases both at primary and recurrence, and in one in four cases only at primary. The presence of NSD1 mutations only at recurrence may suggest that they can be sub-clonal, while the presence in both primary and recurrence implies that they can also represent early and stable events. Furthermore, their presence only in primary, but not in recurrent tumors, suggest that NSD1 mutations may also be influenced by treatment.
Endotoxin in Sepsis: Methods for LPS Detection and the Use of Omics Techniques
Authors: Virzì Grazia Maria , Mattiotti Maria , de Cal Massimo , Ronco Claudio , Zanella Monica , De Rosa Silvia
Published: December 2022

Lipopolysaccharide (LPS) or endotoxin, the major cell wall component of Gram-negative bacteria, plays a pivotal role in the pathogenesis of sepsis. It is able to activate the host defense system through interaction with Toll-like receptor 4, thus triggering pro-inflammatory mechanisms. A large amount of LPS induces inappropriate activation of the immune system, triggering an exaggerated inflammatory response and consequent extensive organ injury, providing the basis of sepsis damage. In this review, we will briefly describe endotoxin’s molecular structure and its main pathogenetic action during sepsis. In addition, we will summarize the main different available methods for endotoxin detection with a special focus on the wider spectrum offered by omics technologies (genomics, transcriptomics, proteomics, and metabolomics) and promising applications of these in the identification of specific biomarkers for sepsis.
Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital Images
Authors: Xin Gangtao , Fan Pingyi , Letaief Khaled B.
Published: December 2022

This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/decoder is O(1/logt). Based on the new finding, an image coding framework with shapes is proposed and proved to be asymptotically optimal for stationary and ergodic processes. Moreover, the condition O(1/logt) of shape-pixel ratio in the encoder/decoder has been confirmed in the image database MNIST, which illustrates the soft compression with shape coding is a near-optimal scheme for lossless compression of images.
Flickering Emergences: The Question of Locality in Information-Theoretic Approaches to Emergence
Authors: Varley Thomas F.
Published: December 2022

“Emergence”, the phenomenon where a complex system displays properties, behaviours, or dynamics not trivially reducible to its constituent elements, is one of the defining properties of complex systems. Recently, there has been a concerted effort to formally define emergence using the mathematical framework of information theory, which proposes that emergence can be understood in terms of how the states of wholes and parts collectively disclose information about the system’s collective future. In this paper, we show how a common, foundational component of information-theoretic approaches to emergence implies an inherent instability to emergent properties, which we call flickering emergence. A system may, on average, display a meaningful emergent property (be it an informative coarse-graining, or higher-order synergy), but for particular configurations, that emergent property falls apart and becomes misinformative. We show existence proofs that flickering emergence occurs in two different frameworks (one based on coarse-graining and another based on multivariate information decomposition) and argue that any approach based on temporal mutual information will display it. Finally, we argue that flickering emergence should not be a disqualifying property of any model of emergence, but that it should be accounted for when attempting to theorize about how emergence relates to practical models of the natural world.
Vanishing Opinions in Latané Model of Opinion Formation
Authors: Dworak Maciej , Malarz Krzysztof
Published: December 2022

In this paper, the results of computer simulations based on the Nowak–Szamrej–Latané model with multiple (from two to five) opinions available in the system are presented. We introduce the noise discrimination level (which says how small the clusters of agents could be considered negligible) as a quite useful quantity that allows qualitative characterization of the system. We show that depending on the introduced noise discrimination level, the range of actors’ interactions (controlled indirectly by an exponent in the distance scaling function, the larger the exponent, the more influential the nearest neighbors are) and the information noise level (modeled as social temperature, which increases results in the increase in randomness in taking the opinion by the agents), the ultimate number of the opinions (measured as the number of clusters of actors sharing the same opinion in clusters greater than the noise discrimination level) may be smaller than the number of opinions available in the system. These are observed in small and large information noise limits but result in either unanimity, or polarization, or randomization of opinions.
Study of Electronic Bands of Diatomic Molecules for the Evaluation of Toxicity of Green Crackers Using LIBS Coupled with Chemometric Method
Authors: Dubey Darpan , Kumar Rohit , Dwivedi Abhishek , Rai Awadhesh Kumar
Published: December 2022

Laser-induced Breakdown Spectroscopy (LIBS) is primarily an atomic emission spectroscopic method based on analyzing the spectral lines of elements in the laser-induced plasma. However, when the plasma cools down after its ignition, i.e., when one collects the emissions from the plasma after a certain interval of time/gate delay (~1 micro-second), the signature of the electronic bands of diatomic molecules is also observed along with ionic/atomic emission lines. The present manuscript reports the evaluation of toxicity/pollutants in green crackers based on the intensity of the electronic bands of the Aluminum Oxide (AlO), calcium oxide (CaO), and strontium oxide (SrO) molecules observed in the laser-induced plasma of the firecrackers. LIBS spectra of the green crackers show the presence of spectral lines of the heavy/toxic elements such as Al, Ca, Sr, Cr, Cu, and Ba, along with the electronic bands of the AlO, CaO, and SrO. Fourier Transform Infra-Red Spectroscopy (FTIR) has been used to validate the LIBS results and confirm the molecules in these crackers. The concentration of toxic elements in green crackers such as Aluminum (Al), Copper (Cu), and Chromium (Cr) has also been estimated using the Partial Least Square Regression method (PLSR) to evaluate and compare the extent of the toxicity of green crackers.
CFD Calculations of Average Flow Parameters around the Rotor of a Savonius Wind Turbine
Authors: Michna Jan , Rogowski Krzysztof
Published: December 2022

The geometry of a conventional two-bladed Savonius rotor was used in this study based on a report available in the literature. A two-dimensional rotor model consisting of two buckets and an overlap ratio of 0.1 was prepared. The unsteady Reynolds averaged Navier-Stokes (URANS) equations and the eddy-viscosity turbulence model SST k-ω were employed in order to solve the fluid motion equations numerically. Instantaneous velocities and pressures were calculated at defined points around the rotor and then averaged. The research shows that the operating rotor significantly modifies the flow on the downwind part of the rotor and in the wake, but the impact of the tip speed ratio on the average velocity distribution is small. This parameter has a much greater influence on the characteristics of the aerodynamic moment and the distribution of static pressure in the wake. In the upwind part of the rotor, the average velocity parallel to the direction of undisturbed flow is 29% lower than in the downwind part.
Identifying and Ranking the Root Causes of Schedule Delays in Oil and Gas Pipeline Construction Projects
Authors: Alshibani Adel , Julaih Mohammed , Adress Ahamd , Alshamrani Othman , Almaziad Faris
Published: December 2022

Delays in completing the construction of oil and gas projects impact the production, sustainability, reliability, and revenues of the company, which result in market share losses in the long term. This research aimed to determine, evaluate, and rank the root causes of schedule delays in oil and gas pipeline projects in Saudi Arabia. Forty-seven different causes were identified firstly from a wide-ranging literature review and then finalized through conducting interviews with two local experts. These causes were ranked by distributing and analyzing questionnaires on the topic. Client-related materials, delays in materials and equipment delivery, permit approval, delays in subcontractor work, and delays in preparing and approving engineering drawings were found to be the top five major causes affecting oil and gas pipeline construction projects. From the initial analyses, it was observed that an adversarial relationship was apparent as the results indicated that the parties tended to blame each other for the delays. The sub-causes of the initial five causes were identified and root cause analysis was performed. A delay in the material and equipment delivery category had the most weighted sub-causes; hence, it is of high importance to mitigate it. Further recommendations were made for different delay causes.
Understanding Energy Behavioral Changes Due to COVID-19 in the Residents of Dubai Using Electricity Consumption Data and Their Impacts
Authors: Manandhar Prajowal , Rafiq Hasan , Rodriguez-Ubinas Edwin , Barbosa Juan David , Qureshi Omer Ahmed , Tarek Mahmoud , Sgouridis Sgouris
Published: December 2022

The building sector consumes as much as 80% of generated electricity in the UAE; during the COVID-19 pandemic, the energy consumption of two sub-sectors, i.e., commercial (50%) and residential (30%), was significantly impacted. The residential sector was impacted the most due to an increase in the average occupancy during the lockdown period. This increment continued even after the lockdown due to the fear of infection. The COVID-19 pandemic and its lockdown measures can be considered experimental setups, allowing for a better understanding of how users shift their consumption under new conditions. The emergency health measures and new social dynamics shaped the residential sector’s energy behavior and its increase in electricity consumption. This article presents and analyzes the identified issues concerning residential electricity consumers and how their behaviors change based on the electricity consumption data during the COVID-19 period. The Dubai Electricity and Water Authority conducted a voluntary survey to define the profiles of its residential customers. A sample of 439 consumers participated in this survey and four years of smart meter records. The analysis focused on understanding behavioral changes in consumers during the COVID-19 period. At this time, the dwellings were occupied for longer than usual, increasing their domestic energy consumption and altering the daily peak hours for the comparable period before, during, and after the lockdown. This work addressed COVID-19 and the lockdown as an atypical case. The authors used a machine learning model and the consumption data for 2018 to predict the consumption for each year afterward, observing the COVID-19 years (2020 and 2021), and compared them with the so-called typical 2019 predictions. Four years of fifteen-minute resolution data and the detailed profiles of the customers led to a better understanding of the impacts of COVID-19 on residential energy use, irrespective of changes caused by seasonal variations. The findings include the reasons for the changes in consumption and the effects of the pandemic. There was a 12% increase in the annual consumption for the sample residents considered in 2020 (the COVID-19-affected year) as compared to 2019, and the total consumption remained similar with only a 0.2% decrease in 2021. The article also reports that machine learning models created in only one year, 2018, performed better by 10% in prediction compared with the deep learning models due to the limited training data available. The article implies the need for exploring approaches/features that could model the previously unseen COVID-19-like scenarios to improve the performance in case of such an event in the future.
De-Loaded Technique Enhanced by Fuzzy Logic Controller to Improve the Resilience of Microgrids Based on Wind Energy and Energy Storage Systems
Authors: Mahrouch Assia , Ouassaid Mohammed , Cabrane Zineb , Lee Soo Hyoung
Published: December 2022

Wind turbine generators (WTGs) are highly sensitive to the disturbances of the grid and tend to disconnect quickly during a voltage dip (when the voltage value is less than 80% of the nominal voltage) or when the frequency is greatly changed. As an increasing number of permanent magnet synchronous generators (PMSGs) are incorporated into the modern power grid, system operators expect PMSG-WT to play an active role in low-voltage ride-through (LVRT) and primary frequency regulation (PFR). Consequently, PMSG-WTs must be capable of supplying additional active power in response to changes in system voltage and frequency. In this context, a new de-loaded technique enhanced by a fuzzy-logic controller is suggested to develop the PMSG-pitch angle control (PMSG-PAC). The studied MG consists of a wind farm (WF), variable load, and a battery energy storage system (BESS). The WF contains five PMSG-WTs which are considered to be the principal resource. The proposed DT-FLC ensures maximum aerodynamic reserve power for the plant, enhances its capability to regulate the PAC, adjusts the WTG drop in response to the wind speed, and increases the resilience of the PMSG-WT in the presence of low voltage. Moreover, the PFR is significantly improved in terms of controlling the PAC (−0.0007 Hz) which meets the frequency maximum droop recommended by the IEEE Std 1547-2018 and the Moroccan grid code, −3 Hz and −2.5 Hz, respectively.
Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor
Authors: Yang Rui , Liu Yongbao , He Xing , Liu Zhimeng
Published: December 2022

Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units.
Thermophysical Properties and Elemental Composition of Black Locust, Poplar and Willow Biomass
Authors: Stachowicz Paweł , Stolarski Mariusz Jerzy
Published: December 2022

Biomass is currently the main renewable energy source (RES) in the EU, particularly in Poland. Solid biomass for energy purposes is primarily sourced from forests, the wood processing industry, and agriculture. A significant source of this energy feedstock could also be short-rotation woody crops (SRWCs), including black locust, poplar, and willow. Since numerous factors determine the SRWC biomass quality, the current study aimed at assessing biomass thermophysical properties and elemental composition depending on the plant species, soil enrichment procedure, and the plant harvest rotation over a consecutive 12-year period of cultivation. The characteristics under study, including the moisture content, ash content, volatile matter, fixed carbon, higher heating value (HHV), and the carbon, hydrogen, nitrogen, sulfur, and chlorine contents, were significantly differentiated by all the main factors, i.e., the SRWC species, the soil enrichment procedure, the harvest rotation, and the interactions between these factors. The SRWC species accounted for the highest percentage of the variation in the biomass moisture content, ash content, HHV, and nitrogen content, while the harvest rotation made the largest contribution to the variation in carbon, hydrogen, and chlorine contents. The black locust biomass was characterized by the significantly lowest moisture content (an average of 38.89%) and the highest sulfur content (an average of 0.033% DM), nitrogen content (an average of 0.91% DM), and chlorine content (an average of 0.032% DM). However, poplar was characterized by the highest HHV (an average of 19.84 GJ Mg−1 DM) and the highest moisture content (56.52% DM), carbon content (56.52% DM), and ash content (an average of 1.67% DM). Willow was characterized by the lowest ash content (an average of 1.67% DM), a medium moisture content, and the lowest nitrogen content (an average of 0.38% DM) and chlorine content (an average of 0.19% DM).
Comparative Analysis of a New Class of Symmetric and Asymmetric Supercapacitors Constructed on the Basis of ITO Collectors
Authors: Gocki Michał , Jakubowska-Ciszek Agnieszka , Pruski Piotr
Published: December 2022

The paper presents the results of research on new electroconductive polymer materials, based on polypyrrole, for the different supercapacitor constructions, i.e., the symmetric and asymmetric constructions. All the supercapacitors considered contain ITO collectors. Measurements of the complex impedance frequency characteristics were performed for these elements using the electrochemical impedance spectroscopy (EIS) method. Selected fractional-order models, known from the literature, have been used to model the impedance of these elements. The Particle Swarm Optimization (PSO) algorithm was used to estimate the model parameters. Selected estimation results, their comparison, and conclusions are also presented in the paper. The type of active electrolyte component has the greatest impact on the shape of the impedance frequency characteristics. In most cases, the highest capacitance values and the smallest resistance values were obtained for asymmetric supercapacitors.
Feasibility Study of Construction of Pumped Storage Power Station Using Abandoned Mines: A Case Study of the Shitai Mine
Authors: Lyu Xin , Yang Ke , Fang Juejing , Tang Jinzhou , Wang Yu
Published: December 2022

Due to the proposal of China’s carbon neutrality target, the traditional fossil energy industry continues to decline, and the proportion of new energy continues to increase. New energy power systems have high requirements for peak shaving and energy storage, but China’s current energy storage facilities are seriously insufficient in number and scale. The unique features of abandoned mines offer considerable potential for the construction of large-scale pumped storage power stations. Several countries have reported the conversion of abandoned mines to pumped storage plants, and a pilot project for the conversion of an underground reservoir group has been formalized in China. A feasibility study that considered the natural conditions, mine conditions, safety conditions, and economic benefits revealed that the construction of pumped storage power stations using abandoned mines could ameliorate several economic, ecological, and social problems, including resource utilization, ecological restoration, and population resettlement. The construction of pumped storage power stations using abandoned mines not only utilizes underground space with no mining value (reduced cost and construction period), but also improves the peak-load regulation and energy storage urgently needed for the development of power grid systems. Combined with the underground space and surface water resources of the Shitai Mine in Anhui, China, a plan for the construction of a pumped storage power station was proposed. The challenges faced by the current project were evaluated, further research suggested, and demonstration projects established in order to help achieve carbon peaking and carbon neutrality goals.
Insulation Degradation Mechanism and Diagnosis Methods of Offshore Wind Power Cables: An Overview
Authors: Lu Baopeng , Li Shuaibing , Cui Yi , Zhao Xiaowei , Zhang Daqi , Kang Yongqiang , Dong Haiying
Published: December 2022

The marine environment in which offshore wind turbines are located is very complex and subjected to a variety of random loads that vary with time and space. As an important component of offshore wind power, the cable also bears the impact of the environment in which most of the turbines are located. Under the long-term action of mechanical stresses such as tension, torsion, and vibration, the cable insulation will crack due to stress fatigue leading to partial discharge, which seriously affects its electrical performance. The study of the mechanism of the change of electrical properties of cable insulation due to mechanical behavior is of great theoretical guidance to improve the reliable operation of cables. This paper first introduces the basic characteristics and operating conditions of torsion-resistant cables and submarine cables. Then the mechanical behavior of the cables is summarized, and the deterioration mechanism and deterioration effect of wind power cable insulation under the influence of multiple factors such as heat, oxygen, and mechanical stress are sorted out. Then, the basic principles of wind power cable operation condition monitoring methods and their characteristics are described. Finally, the relevant methods for the detection of hidden defects inside the insulation are summarized.
Flow and Combustion Characteristics of Wave Rotor–Trapped Vortex Combustor System
Authors: Kang Yiqin , Wang Chenlu , Fang Gangyi , Xing Fei , Chan Shining
Published: December 2022

Breaking through the limit of conventional compression and combustion, wave rotor and trapped vortex combustors are able to improve the thermal efficiency of gas turbines. Detailed two-dimensional numerical simulations based on Ansys Fluent were performed to study the flow and combustion characteristics of the wave rotor–trapped vortex combustor system. The calculated pressure characteristics agree with the experimental results giving a relative error for average pressure of 0.189% at Port 2 and of 0.672% at Port 4. The flow stratification characteristics and the periodic fluctuations were found to benefit the zonal organized combustion in the trapped vortex combustor. For the six cases of different rotor speeds, as the rotor speed increased, the oxygen mass fraction at the combustor inlet rose and then fell. The proportion of exhaust gas recirculation fell at first and then rose, and the combustion mode became unstable with the dominant frequencies of the fluctuations increasing.
Internet Threat Detection in Smart Grids Based on Network Traffic Analysis Using LSTM, IF, and SVM
Authors: Stryczek Szymon , Natkaniec Marek
Published: December 2022

The protection of users of ICT networks, including smart grids, is a challenge whose importance is constantly growing. Internet of Things (IoT) or Internet of Energy (IoE) devices, as well as network resources, store more and more information about users. Large institutions use extensive security systems requiring large and expensive resources. For smart grid users, this becomes difficult. Efficient methods are needed to take advantage of limited sets of traffic features. In this paper, machine learning techniques to verify network events for recognition of Internet threats were analyzed, intentionally using a limited number of parameters. The authors considered three machine learning techniques: Long Short-Term Memory, Isolation Forest, and Support Vector Machine. The analysis is based on two datasets. In the paper, the data preparation process is also described. Eight series of results were collected and compared with other studies. The results showed significant differences between the techniques, the size of the datasets, and the balance of the datasets. We also showed that a more accurate classification could be achieved by increasing the number of analyzed features. Unfortunately, each increase in the number of elements requires more extensive analysis. The work ends with a description of the steps that can be taken in the future to improve the operation of the models and enable the implementation of the described methods of analysis in practice.
Unsupervised Domain Adaptation for Forest Fire Recognition Using Transferable Knowledge from Public Datasets
Authors: Yan Zhengjun , Wang Liming , Qin Kui , Zhou Feng , Ouyang Jineng , Wang Teng , Hou Xinguo , Bu Leping
Published: December 2022

Deep neural networks (DNNs) have driven the recent advances in fire detection. However, existing methods require large-scale labeled samples to train data-hungry networks, which are difficult to collect and even more laborious to label. This paper applies unsupervised domain adaptation (UDA) to transfer knowledge from a labeled public fire dataset to another unlabeled one in practical application scenarios for the first time. Then, a transfer learning benchmark dataset called Fire-DA is built from public datasets for fire recognition. Next, the Deep Subdomain Adaptation Network (DSAN) and the Dynamic Adversarial Adaptation Network (DAAN) are experimented on Fire-DA to provide a benchmark result for future transfer learning research in fire recognition. Finally, two transfer tasks are built from Fire-DA to two public forest fire datasets, the aerial forest fire dataset FLAME and the large-scale fire dataset FD-dataset containing forest fire scenarios. Compared with traditional handcrafted feature-based methods and supervised CNNs, DSAN reaches 82.5% performance of the optimal supervised CNN on the testing set of FLAME. In addition, DSAN achieves 95.8% and 83.5% recognition accuracy on the testing set and challenging testing set of FD-dataset, which outperform the optimal supervised CNN by 0.5% and 2.6%, respectively. The experimental results demonstrate that DSAN achieves an impressive performance on FLAME and a new state of the art on FD-dataset without accessing their labels during training, a fundamental step toward unsupervised forest fire recognition for industrial applications.
Long-Term Responses to Competing Vegetation Management for Pinus radiata
Authors: Vargas Felipe , Gonzalez-Benecke Carlos A. , Ahumada Rodrigo
Published: December 2022

Numerous studies have been carried out to quantify the response to competing vegetation control (CVC) in P. radiata plantations. Most of these publications have reported on the early response in tree growth; however, a knowledge gap exists regarding the growth responses throughout the rotation. In this study, we analyzed the long-term response of P. radiata plantations across a gradient of seven sites in central Chile. Treatments included a no-action control, two spot (circular) areas of competing vegetation control using herbicides around individual P. radiata seedlings (spot herbicide application of 0.75 and 1.5 m), and total competing vegetation control using herbicides. Additionally, three different timings for control regimes were included (0, 1, and 2 years after planting). Competing vegetation biomass abundance during the first growing season ranged from 0.6 to 5.7 Mg ha−1 across all sites. The total competing vegetation control treatment maintained for 2 years (TotalY012) showed the largest gain in stem volume per hectare (VOL) in most of the sites. The sites included in this study showed contrasting values in productivity, having volume yields for the TotalY012 treatment ranging from 238 m3 ha−1 at the site with the lowest annual rainfall (age 12 years) to 471 m3 ha−1 at the southern site (age 14 years). Across all sites, maximum gain in VOL ranged between 21 and 175 m3 ha−1 at age 11 to 14 years and was linearly correlated to the amount of competing biomass controlled during the first year after planting. At the southern, wetter site, plots with only pre-planting spot herbicide application achieved 87% of VOL of plots with TotalY012. Our results suggest that CVC improved the availability of resources at the site for P. radiata seedlings, increasing volume production by reducing environmental constraints to tree growth differentially at each site.
Growth Dynamics of Young Mixed Norway Spruce and Birch Stands in Finland
Authors: Huuskonen Saija , Lahtinen Tuulia , Miina Jari , Uotila Karri , Bianchi Simone , Niemistö Pentti
Published: December 2022

Mixed-species forests in Fennoscandia are of increasing interest because they may improve resilience, biodiversity, and productivity. Currently there is scarce knowledge available of the early growth dynamics of mixed spruce–birch stands in even-aged managed production forests with artificial regeneration of spruce. The main objective of our study was to examine the present state and the past growth dynamics of current single-storied, young spruce–birch (Picea abies (L.) Karst., Betula pendula Roth, Betula pubescens Ehrh.) stands (age 17–29 years), where spruce was planted and birch naturally regenerated, and juvenile management practices (early cleaning and precommercial thinning) were carried out. We inventoried ten such stands in Southern Finland, for a total of twenty plots. For 160 spruces and 160 birch trees, we reconstructed the past diameter and height growth through stem analysis. We analyzed mean stand characteristics by tree species, and we modelled the individual tree height and diameter growth using the mixed effects Chapman–Richards model. Spruces had slower initial height growth, but by the age of about 20 years their height growth rate eventually approached and exceeded that of birches regenerated naturally at the time of spruce planting. The diameter growth of planted spruce exceeded that of birches even sooner (at the age of about 10 years). Thus, spruces are not suppressed by birches, and they may coexist in the same canopy layer in managed stands. Contrary to earlier guidelines, due to the fast growth of planted spruces, birch mixture needs to be maintained already in the first juvenile stand management (i.e., early cleaning). The growth dynamics of young, planted spruce, and naturally regenerated birch allow the establishment and management of such mixtures and also maintenance of the mixture in the future until the end of the rotation, thus improving biodiversity in boreal, planted spruce forests.
Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition
Authors: De Fazio Roberto , Mastronardi Vincenzo , Petruzzi Matteo , De Vittorio Massimo , Visconti Paolo
Published: December 2022

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors.
Genetic Diversity and Differences among Three F1 Families and Two Wild Populations of Genus Scylla Using Microsatellite Markers
Authors: Gao Weifeng , Cui Wenxiao , Wu Fangchun , Chen Huiying , Liu Simin , Guan Mengyun , Saqib Hafiz Sohaib Ahmed , Ye Shaopan , Ikhwanuddin Mhd , Ma Hongyu
Published: December 2022

Genetic diversity is the determinant of the allocation of germplasm resources in the genetic improvement of aquaculture species. In this study, three F1 families, including a hybrid Scylla family (S. paramamosain ♂ × S. serrata ♀), a paternal family of S. paramamosain, a maternal family of S. serrata, and two wild populations, including a paternal population of S. paramamosain and a maternal population of S. serrata, were used to investigate the genetic diversity and genetic difference. The results indicated that 98 alleles of nine microsatellites loci were observed in five Scylla populations. The highest average value of Ho (observed heterozygosity), He (expected heterozygosity), and PIC (polymorphic information content) of the wild S. paramamosain population were 0.790, 0.799, and 0.771, respectively, suggesting the wild paternal population has high genetic diversity. The comparative analysis of PIC, Fst (fixation index), and HWE (Hardy–Weinberg equilibrium) indicated that the paternal S. paramamosain may be more suitable for artificial breeding than the maternal S. serrata from the perspective of allele frequency. Analysis of molecular variance analysis (AMOVA) showed that the total genetic variation mainly occurred within populations (73.28%), demonstrating that artificial breeding may induce genetic differentiation of the family groups of Scylla. The results of the analysis of Fst value, UPGMA (unweighted pair-group mean analysis) dendrogram, and genetic diversity indicated that the F1 hybrid offspring had a close genetic distance and high genetic identity with the paternal S. paramamosain populations. It indicated that the F1 hybrid offspring showed potential paternal genetic affinities and a similar potential for artificial breeding with S. paramamosain. The study will provide valuable information to evaluate the difference in the genetic diversity and population structure between hybrid offspring and distinct parental populations of Scylla.
Protein Arginine Methyltransferase 5 Is Necessary for Embryonic Development in Medaka Oryzias latipes
Authors: Liang Xiaoting , Duan Shi , Yang Qing , Ma Xiaoqin , Li Zhenyu , Yao Qiting , Wu Kongyue , Chang Pei , Feng Gongqing , Hong Wentao , Cao Mengxi , Zhou Qingchun , Zhong Xueping , Zhao Haobin
Published: December 2022

Protein arginine methyltransferase 5 (Prmt5), conserved from yeast to humans, catalyzes arginine’s dimethylation in proteins. Prmt5 is necessary for embryonic development in mice because it maintains embryonic stem cells. However, the embryos of zebrafish (Danio rerio) remain viable with a deficiency in germ cells and sexual development after the knockout of prmt5. Therefore, it was considered whether prmt5 is dispensable during embryogenesis in fish. Medaka (Oryzias latipes), another model fish organism, was used in this experiment. The medaka prmt5 was mutated with Transcription Activator-Like Effector Nucleases (TALEN) causing the premature stopping of transcription. None of the homozygous prmt5 mutant fish were viable, only the heterozygous offspring survived. Quantitative reverse transcription-polymerase chain reaction (qPCR) results showed a significant decrease in octamer-binding transcription factor 4 (oct4), homeobox transcription factor nanog (nanog), vasa, B-cell CLL/lymphoma 2 (bcl2), and the ratio of bcl2 to bax (bcl2 associated x), and a significant increase in caspase3 and caspase8 in the embryos of the heterozygous prmt5 mutant compared with that of the wild type. The results showed that the mutation of prmt5 caused down-regulation of the genes functioning in stemness and up-regulation of the genes in the cascade of cell death. These results suggested that prmt5 is necessary for embryogenesis via maintaining stemness and repressing apoptosis in medaka.
On Fractional Order Model of Tumor Growth with Cancer Stem Cell
Authors: Aliasghari Ghazaleh , Mesgarani Hamid , Nikan Omid , Avazzadeh Zakieh
Published: December 2022

This paper generalizes the integer-order model of the tumour growth into the fractional-order domain, where the long memory dependence of the fractional derivative can be a better fit for the cellular response. This model describes the dynamics of cancer stem cells and non-stem (ordinary) cancer cells using a coupled system of nonlinear integro-differential equations. Our analysis focuses on the existence and boundedness of the solution in correlation with the properties of Mittag-Leffler functions and the fixed point theory elucidating the proof. Some numerical examples with different fractional orders are shown using the finite difference scheme, which is easily implemented and reliably accurate. Finally, numerical simulations are employed to investigate the influence of system parameters on cancer progression and to confirm the evidence of tumour growth paradox in the presence of cancer stem cells.
Spectra of Reduced Fractals and Their Applications in Biology
Authors: Pham Diana T. , Musielak Zdzislaw E.
Published: December 2022

Fractals with different levels of self-similarity and magnification are defined as reduced fractals. It is shown that spectra of these reduced fractals can be constructed and used to describe levels of complexity of natural phenomena. Specific applications to biological systems, such as green algae, are performed, and it is suggested that the obtained spectra can be used to classify the considered algae by identifying spectra associated with them. The ranges of these spectra for green algae are determined and their extension to other biological as well as other natural systems is proposed.
Port Efficiency Based on the Super-Efficiency EBM-DEA-SDM Model: Empirical Evidence from China
Authors: Gu Yimiao , Liu Wanwan , Loh Hui Shan
Published: December 2022

Guangdong Province enjoys a very high economic status in China especially in terms of port construction. In response to the port development directions in China, the Guangdong government released a policy about the construction of Guangdong ports in the next 15 years. Based on the policy, this study proposes to evaluate the port efficiency of major ports in Guangdong Province during 2011–2020 using the Super-efficiency EBM-DEA model that considers undesirable outputs, and the spatial effect of port efficiency and its influencing factors is further analyzed using the spatial Durbin model. The empirical results shows that the overall port efficiency in Guangdong Province is not high and varies widely among port clusters, thereby lacking synergistic development. The results of the spatial Durbin model show that port efficiency is positively correlated with the level of economic development, port-city relationship and transportation structure, as well as negatively correlated with the efficiency of neighboring ports. The findings have a far-reaching impact on the development of port construction.
Identification and Validation of Three m6A Regulators: FTO, HNRNPC, and HNRNPA2B1 as Potential Biomarkers for Endometriosis
Authors: Sun Jiani , Gan Lei , Sun Jing
Published: December 2022

Background: N6-methyladenosine is involved in numerous biological processes. However, the significance of m6A regulators in endometriosis is still unclear. Methods: We extracted three significant m6A regulators between non-endometriosis and endometriosis patients from GSE6364 and then we used the random forest model to obtain significant m6A regulators. In addition, we used the nomogram model to evaluate the prevalence of endometriosis. The predictive ability of the candidate genes was evaluated through the receiver operating characteristic curves, while the expression of candidate biomarkers was validated via Western blotting. Additionally, according to candidate genes, we identified m6A subtypes based on which functional enrichment analysis and immune infiltration were performed. Results: Three significant m6A regulators (fat mass and obesity-associated protein, heterogeneous nuclear ribonucleoprotein A2/B1, and heterogeneous nuclear ribonucleoprotein C) were discovered. We identified three m6A subtypes, including clusterA, clusterB, and clusterC. ClusterB was demonstrated to be correlated with significantly overexpressed VEGF and notably downregulated ESR1 and PGR, which are convincing biomarkers of endometriosis. Furthermore, we discovered that patients in clusterB were associated with high levels of neutrophil infiltration, a reduced Treg/Th17 ratio, and overexpressed pyroptosis-related genes, which also indicated that clusterB was highly linked to endometriosis. Conclusion: In conclusion, m6A regulators are of great significance for the occurrence and process of endometriosis. The findings of our study provide novel insights into the underlying molecular mechanism of endometriosis. The novel investigation of m6A patterns and their correlation with immunity may also help to guide the clinical diagnosis, provide prognostic significance, and develop immunotherapy strategies for endometriosis patients.
ITS1 Barcode and Phytochemical Analysis by Gas Chromatography–Mass Spectrometry of Corynaea crassa Hook. f (Balanophoraceae) from Ecuador and Peru
Authors: López-Barrera Alexandra , Santos-Ordóñez Efrén , Pacheco-Coello Ricardo , Villao-Uzho Liliana , Miranda Migdalia , Gutiérrez Yamilet , Chóez-Guaranda Iván , Ruiz-Reyes Segundo Guillermo
Published: December 2022

The use of medicinal plants is the basis of traditional healthcare. Recently, the use of herbal medicine has been increasing among consumers due to availability, economy, and less side effect. For instance, the hemiparasite plant Corynaea crassa has medicinal properties and could be found in some regions of America, from Costa Rica to Bolivia. Phytochemical and genetic characterization of medicinal plants is needed for proper identification of metabolites responsible for medicinal properties and for genotyping, respectively. Moreover, characterization of medicinal plants through the use of DNA barcodes is an important tool for phylogenetic analysis and identification of species; furthermore, complemented with phytochemical analysis, both are useful for identification of plant species and quality control of medicinal products. The objective of this study was to analyze the species of C. crassa collected in Ecuador and Peru from the phylogenetic and phytochemical point of view. Polymerase chain reaction (PCR) was performed for amplification of the internal transcribed spacer 1 (ITS1) region after DNA extraction of samples of C. crassa. Blast analysis was performed in the GenBank database with the ITS1 sequences obtained from two accessions of C. crassa from Ecuador (GenBank accession numbers OM471920 and OM471919 for isolates CIBE-17 and CIBE-18, respectively) and three from Peru (GenBank accession numbers OM471921, OM471922, and OM471923 for isolates CIBE-13, CIBE-14, and CIBE-15, respectively). The accessions available in the GenBank were used for phylogenetic analysis. For the phytochemical analysis, hydroalcoholic extracts were obtained by maceration using 80% ethanol as solvent, followed by a derivatization process and analysis by gas chromatography–mass spectrometry. Based on the phylogenetic analysis of the C. crassa samples, the ITS1 sequence could be used to differentiate C. crassa of different locations. The samples of C. crassa from Ecuador and Peru are more similar between them than with other clades including Helosis spp. The phytochemical study revealed differences in the presence and relative abundance of some metabolites; mainly eugenol, 1,4-lactone arabinonic acid, dimethoxyrabelomycin and azelaic acid, which are reported for the first time for the species under study and the genus Corynaea. These results are the first findings on the combined analysis using genetic and phytochemical analysis for C. crassa, which could be used as a useful tool for quality control of the C. crassa species in medicinal products.