bluuebunny/crossref_metadata_2025 huggingface dataset:
A Systematic Review and Meta-Analysis of Reading and Writing Interventions for Students with Disorders of Intellectual Development
Authors: Bakken Randi Karine , Næss Kari-Anne B. , Lemons Christopher J. , Hjetland Hanne Næss
Published: October 2021

Students with disorders of intellectual development (ID) experience challenges in reading and writing, indicating the need for research-based interventions. This systematic review and meta-analysis investigated the effects of reading and writing interventions for students aged 4–19 with disorders of ID using randomized controlled trials (RCTs) and quasi-experimental designs (QEDs). We conducted electronic searches of relevant databases, backward and forward searches, and contacted experts in the field. Based on predefined criteria, nine studies were included in the systematic review, and seven were included in the meta-analysis. The reading interventions included decoding strategies, often combined with sight-word and supplemental instructions appropriate to the participants’ adaptive and cognitive skills. None of the studies aimed to increase writing skills. The overall mean effect size from the reading interventions for trained reading was large (g = 0.95, 95% CI = [0.51, 1.38]), for transfer reading small-to-moderate (g = 0.49, 95% CI = [0.20, 0.78]) and for transfer writing small (g = 0.04, 95% CI = [−0.36, 0.44]). Students with disorders of ID can benefit from reading interventions combining decoding strategies and sight word reading. There is a need for RCT and QED studies investigating writing interventions for students with disorders of ID only.
Analysis of Data-Based Scientific Reasoning from a Product-Based and a Process-Based Perspective
Authors: Meister Sabine , Upmeier zu Belzen Annette
Published: October 2021

In this study, we investigated participants’ reactions to supportive and anomalous data in the context of population dynamics. Based on previous findings on conceptions about ecosystems and responses to anomalous data, we assumed a tendency to confirm the initial prediction after dealing with contradicting data. Our aim was to integrate a product-based analysis, operationalized as prediction group changes with process-based analyses of individual data-based scientific reasoning processes to gain a deeper insight into the ongoing cognitive processes. Based on a theoretical framework describing a data-based scientific reasoning process, we developed an instrument assessing initial and subsequent predictions, confidence change toward these predictions, and the subprocesses data appraisal, data explanation, and data interpretation. We analyzed the data of twenty pre-service biology teachers applying a mixed-methods approach. Our results show that participants tend to maintain their initial prediction fully or change to predictions associated with a mix of different conceptions. Maintenance was observed even if most participants were able to use sophisticated conceptual knowledge during their processes of data-based scientific reasoning. Furthermore, our findings implicate the role of confidence changes and the influences of test wiseness.
Performance Evaluation of NVMe-over-TCP Using Journaling File Systems in International WAN
Authors: Yu Se-young
Published: October 2021

Distributing Big Data for science is pushing the capabilities of networks and computing systems. However, the fundamental concept of copying data from one machine to another has not been challenged in collaborative science. As recent storage system development uses modern fabrics to provide faster remote data access with lower overhead, traditional data movement using Data Transfer Nodes must cope with the paradigm shift from a store-and-forward model to streaming data with direct storage access over the networks. This study evaluates NVMe-over-TCP (NVMe-TCP) in a long-distance network using different file systems and configurations to characterize remote NVMe file system access performance in MAN and WAN data moving scenarios. We found that NVMe-TCP is more suitable for remote data read than remote data write over the networks, and using RAID0 can significantly improve performance in a long-distance network. Additionally, a fine-tuning file system can improve remote write performance in DTNs with a long-distance network.
Classical Music Specific Mood Automatic Recognition Model Proposal
Authors: Lee Suyeon , Jeong Haemin , Ko Hyeyoung
Published: October 2021

The purpose of this study was to propose an effective model for recognizing the detailed mood of classical music. First, in this study, the subject classical music was segmented via MFCC analysis by tone, which is one of the acoustic features. Short segments of 5 s or under, which are not easy to use in mood recognition or service, were merged with the preceding or rear segment using an algorithm. In addition, 18 adjective classes that can be used as representative moods of classical music were defined. Finally, after analyzing 19 kinds of acoustic features of classical music segments using XGBoost, a model was proposed that can automatically recognize the music mood through learning. The XGBoost algorithm that is proposed in this study, which uses the automatic music segmentation method according to the characteristics of tone and mood using acoustic features, was evaluated and shown to improve the performance of mood recognition. The result of this study will be used as a basis for the production of an affect convergence platform service where the mood is fused with similar visual media when listening to classical music by recognizing the mood of the detailed section.
Identifying Symmetric-Key Algorithms Using CNN in Intel Processor Trace
Authors: Yang Wooyeol , Park Yongsu
Published: October 2021

Malware and ransomware are often encrypted to protect their own code, making it challenging to apply reverse engineering to analyze them. Recently, various studies have been underway to identify cryptography algorithms in malware or ransomware that use anti-reversing technology via deep-learning technology. In particular, CNNs (convolution neural networks) are deep-learning algorithms with superior performance, as compared to existing machine-learning algorithms in image classification. In the cases of malicious files to which anti-debugging techniques or anti-DBI (dynamic binary instrumentation) techniques are applied, if the traces are extracted using various debuggers or DBI, the traces are cut off due to these techniques. The IPT (Intel processor trace) has the advantage of extracting an accurate trace of a program by bypassing the anti-debugging or anti-DBI technique. This paper presents a novel method by which to identify the symmetric-key algorithms by applying a CNN to the traces extracted from the IPT. The IPT minimally interrupts software execution. First, the trace encrypted by the symmetric-key algorithms is extracted using the IPT. Then it is converted into an image to be an input into the CNN. The experiments were carried out with two different datasets. The first dataset contained traces extracted by different types of symmetric-key algorithms, and the training results were classified into nine classes with 100% accuracy. The second dataset contained traces that included the various bit sizes of the security keys and the block-cipher modes for each type of symmetric-key algorithm. Training results were classified into 36 classes with an accuracy of 70.55%. While previous studies have identified the types of encryption algorithms, this study employed a CNN to identify the number of key bits and the block-cipher modes as well.
Technique for Profiling the Cycling-Induced Oxide Trapped Charge in NAND Flash Memories
Authors: Chiu Yung-Yueh , Shirota Riichiro
Published: October 2021

NAND Flash memories have gained tremendous attention owing to the increasing demand for storage capacity. This implies that NAND cells need to scale continuously to maintain the pace of technological evolution. Even though NAND Flash memory technology has evolved from a traditional 2D concept toward a 3D structure, the traditional reliability problems related to the tunnel oxide continue to persist. In this paper, we review several recent techniques for separating the effects of the oxide charge and tunneling current flow on the endurance characteristics, particularly the transconductance reduction (ΔGm,max) statistics. A detailed analysis allows us to obtain a model based on physical measurements that captures the main features of various endurance testing procedures. The investigated phenomena and results could be useful for the development of both conventional and emerging NAND Flash memories.
Next-Generation Blockchain-Enabled Virtualized Cloud Security Solutions: Review and Open Challenges
Authors: Uddin Mueen , Khalique Anjum , Jumani Awais Khan , Ullah Syed Sajid , Hussain Saddam
Published: October 2021

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective IT solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from the grid and parallel computing models. It uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better IT solutions for large-scale computational data centres. It encompasses different services for supporting data storage, networking, and computing for facilities and amenities for businesses and multinational corporations. The enormous elastic on-demand cloud provisioning resources and services and datasets are processed and stored in tier-level virtualized cloud data centres operated by third-party service providers called cloud owners. The primary issue with these cloud service providers is to provide and maintain data security, privacy, and confidentiality and service availability and data support for end-users. This paper reviews, highlights, and discusses some of the common cloud computing vulnerabilities primarily related to virtualization platforms and their implementations while outsourcing services and resources to different end-users and business enterprises. We then provided blockchain-enabled solutions for virtualized cloud platforms involving both the end-users and cloud service providers (CSP) to address and solve various security and privacy-related vulnerabilities. These solutions will help the data centre industry to improve its virtualized cloud services and resource provisioning facilities. Finally, we discussed different blockchain-related implementation challenges in cloud infrastructures.
A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks
Authors: Tsantikidou Kyriaki , Tampouratzis Nikolaos , Papaefstathiou Ioannis
Published: October 2021

In recent years, systems that monitor and control home environments, based on non-vocal and non-manual interfaces, have been introduced to improve the quality of life of people with mobility difficulties. In this work, we present the reconfigurable implementation and optimization of such a novel system that utilizes a recurrent neural network (RNN). As demonstrated in the real-world results, FPGAs have proved to be very efficient when implementing RNNs. In particular, our reconfigurable implementation is more than 150× faster than a high-end Intel Xeon CPU executing the reference inference tasks. Moreover, the proposed system achieves more than 300× the improvements, in terms of energy efficiency, when compared with the server CPU, while, in terms of the reported achieved GFLOPS/W, it outperforms even a server-tailored GPU. An additional important contribution of the work discussed in this study is that the implementation and optimization process demonstrated can also act as a reference to anyone implementing the inference tasks of RNNs in reconfigurable hardware; this is further facilitated by the fact that our C++ code, which is tailored for a high-level-synthesis (HLS) tool, is distributed in open-source, and can easily be incorporated to existing HLS libraries.
A Simple and Robust Equalization Algorithm for Variable Modulation Systems
Authors: Hu Wanru , Wang Zhugang , Mei Ruru , Lin Meiyan
Published: October 2021

This paper proposes a simple and robust variable modulation-decision-directed least mean square (VM-DDLMS) algorithm for reducing the complexity of conventional equalization algorithms and improving the stability of variable modulation (VM) systems. Compared to conventional adaptive equalization algorithms, known information was used as training sequences to reduce the bandwidth consumption caused by inserting training sequences; compared with conventional blind equalization algorithms, the parameters and decisions of the equalizer were determinate, which was conducive to a stable equalization performance. The simulation and implementation results show that the proposed algorithm has a better bit error rate (BER) performance than that of the constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA) while maintaining the same level of consumption of hardware resources. Compared to the conventional decision-directed least mean square (DDLMS) algorithm, the proposed algorithm only needs to make quadrature phase shift keying (QPSK) symbol decisions, which reduces the computational complexity. In parallel 11th-order equalization algorithms, the operating frequency of VM-DDLMS can reach up to 333.33 MHz.
Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems
Authors: Andronie Mihai , Lăzăroiu George , Iatagan Mariana , Uță Cristian , Ștefănescu Roxana , Cocoșatu Mădălina
Published: October 2021

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.
A Novel Fundamental Frequency Switching Operation for Conventional VSI to Enable Single-Stage High-Gain Boost Inversion with ANN Tuned QWS Controller
Authors: Tripathi Prabhat R. , Laxmi V. , Keshri Ritesh K. , Appasani Bhargav , Ustun Taha Selim
Published: October 2021

Single-stage high-gain inverters have recently gained much research focus as interfaces for inherent low voltage DC sources such as fuel cells, storage batteries, and solar panels. Many impedance-assisted inverters with different input stage configurations have been presented. To decrease passive component sizes, these inverters operate at high-frequency switching. The high-frequency switching optimizes the passive component sizes but introduces many challenges in the form of high-frequency inductor design, control complexity, high-frequency gate driver requirements, high semiconductor losses, and electromagnetic interferences. This article proposes a novel fundamental frequency switching operation for the conventional voltage source inverters (VSI) to operate as a single-stage high-gain inverter. As the novel operational strategy changes the behavior of conventional VSI from buck inverter to a boost inverter, it is hereafter termed as a novel inverter. By virtue of the operation strategy, switches withstand peak inverse voltage (PIV) equal to DC link voltage, unlike other impedance assisted boost inverters where PIV across switches is the amplified DC voltage. The proposed inverter can invert low-level DC voltage to high voltage AC with low total harmonic distortion (THD) in a single stage without the help of any external filter. A novel quarter-wave symmetric phase-shift controller is proposed for variable voltage and frequency control of proposed inverters tuned by a back-propagation thin-plate-spline neural network (BPTPSNN). Mathematical analysis with experimental validation is presented. Experimentation is carried out on a prototype of 2 kW for single-phase resistive load, induction motor, and non-linear loads.
MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks
Authors: Ta Vinh Quoc , Park Minho
Published: October 2021

Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies.
Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions
Authors: Imran Muhammad , Zaman Umar , Imran  , Imtiaz Junaid , Fayaz Muhammad , Gwak Jeonghwan
Published: October 2021

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as ‘machine learning’, blockchain, ‘Internet of Things or IoT’, and keywords conjoined with ‘healthcare’ and ‘health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.
ML-CLOCK: Efficient Page Cache Algorithm Based on Perceptron-Based Neural Network
Authors: Cho Minseon , Kang Donghyun
Published: October 2021

Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms.
Abundance and Composition of Marine Litter on the Seafloor of the Gulf of Sant Jordi (Western Mediterranean Sea)
Authors: Saladié Òscar , Bustamante Edgar
Published: October 2021

This article analyzes the abundance and composition of marine litter in the Gulf of Sant Jordi (Catalonia, Spain). Marine litter was removed from the sea by a fishing trawler operating from the port of L’Ametlla de Mar; 56 hauls were performed between July and September 2018. The marine litter was classified following UNEP/IOC Guidelines on Survey and Monitoring of Marine Litter and EU MSFD Technical Group on Marine Litter Joint List, with a total of 2691 items collected and an average number by haul of 48 (SD 28.24). The density was 130 items km−2 but with significant differences according to trawling depth: 192 items km−2 (≤100 m) and 71.5 items km−2 (>100 m). As expected, plastic was the most commonly found material, comprising almost 80% of the total. The relative presence of plastics declined as trawling depth increased. An alarmingly high amount of sanitary waste was found. Further studies are necessary to compare summer results with those of smaller seasonal populations and to analyze what happens to sanitary waste.
A Register Access Control Scheme for SNR System to Counter CPA Attack Based on Malicious User Blacklist
Authors: Shi Jia , Zeng Xuewen , Li Yang
Published: October 2021

Standalone Name Resolution (SNR) is an essential component of many Information-Centric Networking (ICN) infrastructures that maps and stores the mappings of IDs and locators. The delivery of data can be realized only when the name resolution process is completed correctly. It also makes the SNR become the key target of network attackers. In this paper, our research focuses on the more covert and complex Content Pollution Attack (CPA). By continuously sending invalid content to the network at a low speed, attackers will consume a lot of the resources and time of the SNR system, resulting in a serious increase in the resolution delay of normal users and further cache pollution in ICN. It is difficult to be quickly detected because the characteristics of attack are inconspicuous. To address the challenge, a register access control scheme for an SNR system based on a malicious user blacklist query is proposed. A neighbor voting algorithm is designed to discover possible attacks in the network quickly and build a blacklist of malicious users reasonably. Users on the blacklist will be restricted from accessing the ICN network during the registration phase with the resolution system. Incentives and punishments for network users are introduced to automate responses about the potential malicious behavior reports. Our scheme is more efficient as users do not have to wait for an additional system component to perform operations. In addition, our algorithm can better solve the collusion problem in the voting process when compared with the others. We experimentally evaluate our protocol to demonstrate that the probability of successful collusion attack can be reduced to less than 0.1 when the attacker ratio is 0.5.
Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
Authors: Hilda Jabanjalin , Chandrasekaran Srimathi
Published: October 2021

A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms—such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH)), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)—while holding the same time complexity.
Large-Eddy Simulation of a Hydrocyclone with an Air Core Using Two-Fluid and Volume-of-Fluid Models
Authors: Fayed Hassan , Bukhari Mustafa , Ragab Saad
Published: October 2021

Large-eddy simulations have been conducted for two-phase flow (water and air) in a hydrocyclone using Two-Fluid (Euler–Euler) and Volume-of-Fluid (VOF) models. Subgrid stresses are modeled using a dynamic eddy–viscosity model, and results are compared to those using the Smagorinsky model. The effects of grid resolutions on the mean flow and turbulence statistics have been thoroughly investigated. Five block-structured grids of 0.72, 1.47, 2.4, 3.81, and 7.38 million elements have been used for the simulations of Hsieh’s 75 mm hydrocyclone Mean velocity profiles and normal Reynolds stresses have been compared with experimental data. Results of the two-fluid model are in good agreement with those of the VOF model. A fine mesh in the axial and radial directions is necessary for capturing the turbulent vortical structure. Turbulence structures in the hydrocyclone are dominated by helical vortices around the air core. Energy spectra are analyzed at different points in the hydrocyclone, and regions of low turbulent kinetic energy are identified and attributed to stabilizing effects of the swirling velocity component.
Approximating Real-Life BVPs via Chebyshev Polynomials’ First Derivative Pseudo-Galerkin Method
Authors: Abdelhakem Mohamed , Alaa-Eldeen Toqa , Baleanu Dumitru , Alshehri Maryam G. , El-Kady Mamdouh
Published: October 2021

An efficient technique, called pseudo-Galerkin, is performed to approximate some types of linear/nonlinear BVPs. The core of the performance process is the two well-known weighted residual methods, collocation and Galerkin. A novel basis of functions, consisting of first derivatives of Chebyshev polynomials, has been used. Consequently, new operational matrices for derivatives of any integer order have been introduced. An error analysis is performed to ensure the convergence of the presented method. In addition, the accuracy and the efficiency are verified by solving BVPs examples, including real-life problems.
A Roadmap to Gamma-Ray Bursts: New Developments and Applications to Cosmology
Authors: Luongo Orlando , Muccino Marco
Published: October 2021

Gamma-ray bursts are the most powerful explosions in the universe and are mainly placed at very large redshifts, up to z≃9. In this short review, we first discuss gamma-ray burst classification and morphological properties. We then report the likely relations between gamma-ray bursts and other astronomical objects, such as black holes, supernovae, neutron stars, etc., discussing in detail gamma-ray burst progenitors. We classify long and short gamma-ray bursts, working out their timescales, and introduce the standard fireball model. Afterwards, we focus on direct applications of gamma-ray bursts to cosmology and underline under which conditions such sources would act as perfect standard candles if correlations between photometric and spectroscopic properties were not jeopardized by the circularity problem. In this respect, we underline how the shortage of low-z gamma-ray bursts prevents anchor gamma-ray bursts with primary distance indicators. Moreover, we analyze in detail the most adopted gamma-ray burst correlations, highlighting their main differences. We therefore show calibration techniques, comparing such treatments with non-calibration scenarios. For completeness, we discuss the physical properties of the correlation scatters and systematics occurring during experimental computations. Finally, we develop the most recent statistical methods, star formation rate, and high-redshift gamma-ray burst excess and show the most recent constraints obtained from experimental analyses.
An Investigation of the Role of Common and Rare Variants in a Large Italian Multiplex Family of Multiple Sclerosis Patients
Authors: Barizzone Nadia , Cagliani Rachele , Basagni Chiara , Clarelli Ferdinando , Mendozzi Laura , Agliardi Cristina , Forni Diego , Tosi Martina , Mascia Elisabetta , Favero Francesco , Corà Davide , Corrado Lucia , Sorosina Melissa , Esposito Federica , Zuccalà Miriam , Vecchio Domizia , Liguori Maria , Comi Cristoforo , Comi Giancarlo , Martinelli Vittorio , Filippi Massimo , Leone Maurizio , Martinelli-Boneschi Filippo , Caputo Domenico , Sironi Manuela , Guerini Franca Rosa , D’Alfonso Sandra
Published: October 2021

Known multiple sclerosis (MS) susceptibility variants can only explain half of the disease’s estimated heritability, whereas low-frequency and rare variants may partly account for the missing heritability. Thus, here we sought to determine the occurrence of rare functional variants in a large Italian MS multiplex family with five affected members. For this purpose, we combined linkage analysis and next-generation sequencing (NGS)-based whole exome and whole genome sequencing (WES and WGS, respectively). The genetic burden attributable to known common MS variants was also assessed by weighted genetic risk score (wGRS). We found a significantly higher burden of common variants in the affected family members compared to that observed among sporadic MS patients and healthy controls (HCs). We also identified 34 genes containing at least one low-frequency functional variant shared among all affected family members, showing a significant enrichment in genes involved in specific biological processes—particularly mRNA transport—or neurodegenerative diseases. Altogether, our findings point to a possible pathogenic role of different low-frequency functional MS variants belonging to shared pathways. We propose that these rare variants, together with other known common MS variants, may account for the high number of affected family members within this MS multiplex family.
Methanogenesis and Salt Tolerance Genes of a Novel Halophilic Methanosarcinaceae Metagenome-Assembled Genome from a Former Solar Saltern
Authors: Bueno de Mesquita Clifton P. , Zhou Jinglie , Theroux Susanna M. , Tringe Susannah G.
Published: October 2021

Anaerobic archaeal methanogens are key players in the global carbon cycle due to their role in the final stages of organic matter decomposition in anaerobic environments such as wetland sediments. Here we present the first draft metagenome-assembled genome (MAG) sequence of an unclassified Methanosarcinaceae methanogen phylogenetically placed adjacent to the Methanolobus and Methanomethylovorans genera that appears to be a distinct genus and species. The genome is derived from sediments of a hypersaline (97–148 ppt chloride) unrestored industrial saltern that has been observed to be a significant methane source. The source sediment is more saline than previous sources of Methanolobus and Methanomethylovorans. We propose a new genus name, Methanosalis, to house this genome, which we designate with the strain name SBSPR1A. The MAG was binned with CONCOCT and then improved via scaffold extension and reassembly. The genome contains pathways for methylotrophic methanogenesis from trimethylamine and dimethylamine, as well as genes for the synthesis and transport of compatible solutes. Some genes involved in acetoclastic and hydrogenotrophic methanogenesis are present, but those pathways appear incomplete in the genome. The MAG was more abundant in two former industrial salterns than in a nearby reference wetland and a restored wetland, both of which have much lower salinity levels, as well as significantly lower methane emissions than the salterns.
FLT4 Mutations Are Associated with Segmental Lymphatic Dysfunction and Initial Lymphatic Aplasia in Patients with Milroy Disease
Authors: Liu Ningfei , Gao Minzhe
Published: October 2021

This study explored mutations in the Fms-related tyrosine kinase 4/vascular endothelial growth factor receptor 3 gene (FLT4) and lymphatic defects in patients with Milroy disease (MD). Twenty-nine patients with lower limb lymphedema were enrolled. Sixteen patients had a familial history of MD, while 13 patients exhibited sporadic MD. Clinical signs, FLT4 mutations, indocyanine green (ICG) lymphography findings, and skin tissue immunohistochemical staining results were evaluated. Twenty-eight variants in FLT4 were identified. Twelve of these have previously been reported, while 16 are novel. Of the 28 variants, 26 are missense mutations, and the remaining two comprise a splicing mutation and a non-frame shift mutation. Twenty-five variants are located in the intracellular protein tyrosine kinase domain; three are located in the extracellular immunoglobulin domain. Substantially delayed contrast-enhanced tortuous lymphatic vessels were visualized to the ankle or knee level in 15 of 23 patients who underwent ICG lymphography. No initial lymphatic vessels were visualized in skin specimens from four patients who did not exhibit lymphatic vessels during imaging analyses. No specific variant was identified in relation to the unique clinical phenotype. Segmental dysfunction of lymphatic vessels and initial lymphatic aplasia are present in MD patients with FLT4 mutations.
Epigenetic Regulatory Dynamics in Models of Methamphetamine-Use Disorder
Authors: Jayanthi Subramaniam , McCoy Michael T. , Cadet Jean Lud
Published: October 2021

Methamphetamine (METH)-use disorder (MUD) is a very serious, potentially lethal, biopsychosocial disease. Exposure to METH causes long-term changes to brain regions involved in reward processing and motivation, leading vulnerable individuals to engage in pathological drug-seeking and drug-taking behavior that can remain a lifelong struggle. It is crucial to elucidate underlying mechanisms by which exposure to METH leads to molecular neuroadaptive changes at transcriptional and translational levels. Changes in gene expression are controlled by post-translational modifications via chromatin remodeling. This review article focuses on the brain-region specific combinatorial or distinct epigenetic modifications that lead to METH-induced changes in gene expression.
Isolation of RNA from Acute Ischemic Stroke Clots Retrieved by Mechanical Thrombectomy
Authors: Tutino Vincent M. , Fricano Sarah , Frauens Kirsten , Patel Tatsat R. , Monteiro Andre , Rai Hamid H. , Waqas Muhammad , Chaves Lee , Poppenberg Kerry E. , Siddiqui Adnan H.
Published: October 2021

Mechanical thrombectomy (MT) for large vessel acute ischemic stroke (AIS) has enabled biologic analyses of resected clots. While clot histology has been well-studied, little is known about gene expression within the tissue, which could shed light on stroke pathophysiology. In this methodological study, we develop a pipeline for obtaining useful RNA from AIS clots. A total of 73 clot samples retrieved by MT were collected and stored in RNALater and in 10% phosphate-buffered formalin. RNA was extracted from all samples using a modified Chemagen magnetic bead extraction protocol on the PerkinElmer Chemagic 360. RNA was interrogated by UV–Vis absorption and electrophoretic quality control analysis. All samples with sufficient volume underwent traditional qPCR analysis and samples with sufficient RNA quality were subjected to next-generation RNA sequencing on the Illumina NovaSeq platform. Whole blood RNA samples from three patients were used as controls, and H&E-stained histological sections of the clots were used to assess clot cellular makeup. Isolated mRNA was eluted into a volume of 140 µL and had a concentration ranging from 0.01 ng/µL to 46 ng/µL. Most mRNA samples were partially degraded, with RNA integrity numbers ranging from 0 to 9.5. The majority of samples (71/73) underwent qPCR analysis, which showed linear relationships between the expression of three housekeeping genes (GAPDH, GPI, and HPRT1) across all samples. Of these, 48 samples were used for RNA sequencing, which had moderate quality based on MultiQC evaluation (on average, ~35 M reads were sequenced). Analysis of clot histology showed that more acellular samples yielded RNA of lower quantity and quality. We obtained useful mRNA from AIS clot samples stored in RNALater. qPCR analysis could be performed in almost all cases, while sequencing data could only be performed in approximately two-thirds of the samples. Acellular clots tended to have lower RNA quantity and quality.
Preliminary Studies of the Effects of Nanoconsolidants on Mural Paint Layers with a Lack of Cohesion
Authors: Baiza Berenice , Gil Milene , Galacho Cristina , Candeias António , Girginova Penka I.
Published: October 2021

This paper reports the preliminary results of a comparative analysis of the effects of three consolidants on the color appearance of fresco paint layers affected by lack of cohesion. In vitro assays were performed with a laboratory-synthesized nanolime, a commercial nanolime (CaLoSiL® IP25), and a commercial acrylic resin (PrimalTM SF-016 ER®) applied by nebulization over two sets of replicas of buon and lime fresco painted with red and yellow ochres and smalt pigments. The paint layers were surveyed before, one week, and one month after treatment with technical photography in the visible range (Vis) and ultraviolet-induced fluorescence in the visible range (UVF), as well as optical microscopy (OM-Vis), colorimetry, spectrophotometry, and scanning electron microscopy coupled with energy dispersive x-ray spectroscopy (SEM-EDS). Experimental work also comprised the synthesis of nanolime and its characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetry analysis (TGA-DTG). The results show no alteration on pigments’ spectral curves and elemental composition. The increase in the CIEL* coordinate and ∆E color variation noticed after the treatment with the nanolimes is associated with a white haze formation on the paint surfaces. The impact on color appearance is higher on the darker tones.
Generation of a Multi-Scale Historic BIM-GIS with Digital Recording Tools and Geospatial Information
Authors: Barazzetti Luigi , Roncoroni Fabio
Published: October 2021

This paper discusses the creation of an integrated historic BIM-GIS for the complex of San Pietro al Monte, an important Romanesque monument in Civate (Italy) inscribed in the UNESCO tentative list with other seven medieval Benedictine settlements. The reason behind the choice of an integrated H-BIM-GIS solution is motivated by the large extension of the considered area (about 30 km2) and the need for multi-scale digital information integrated into a 3D parametric environment. The model includes geospatial information at a territorial scale and in situ digital data capturing the complex at a higher level of detail. The work aims at exploring the pros and cons of a novel parametric 3D environment able to integrate both BIM and GIS data, methods, and processing tools in the case of historic buildings and sites.
The Innovative and State of the Art Public Access Management of Malta’s Underwater Cultural Heritage
Authors: Gambin Timmy , Sausmekat Maja , Kovacevic Darko
Published: October 2021

The obligation to preserve underwater cultural heritage is a core principle of the UNESCO Convention on the Protection of the Underwater Cultural Heritage. A key element of this obligation is a balance of scientific research, protection, and the promotion of responsible access to underwater cultural heritage sites. Such a balance requires the setting up of a network of communication between the tourism and heritage sectors on the one hand and the maritime and diving communities on the other hand. A variety of approaches have been developed to promote responsible access to underwater cultural heritage sites, and since the vast majority of the public does not dive, this also includes the development of virtual access. In Malta, maritime archaeology can be traced to humble and sporadic beginnings in the 1950s. The following decades brought a growing interest in diving activities and a rising number of diving schools and clubs. Whilst Malta has today established itself as a diving tourism destination, responsible access to underwater cultural heritage sites was not always entrenched in dive operations or institutionally recognized. The protection and management of underwater cultural heritage has recently been firmly established within Heritage Malta, the national agency for museums, conservations, and cultural heritage. This paper is intended to outline the trajectory of Malta’s underwater cultural heritage management and to present the innovative and state of the art public access system that is managed by the Underwater Cultural Heritage Unit within Heritage Malta.
Analysis of Glucosinolate Content, Composition and Expression Level of Biosynthesis Pathway Genes in Different Chinese Kale Varieties
Authors: Tong Lu , Cheng Shanhan , Lv Honghao , Zhao Chengzhi , Zhu Jie , Liu Pingwu , Wang Zhiwei , Yang Limei , Zhang Yangyong
Published: October 2021

The content and component of glucosinolates in edible stems and leaves of eight Chinese kale varieties from Japan and eight varieties from China were determined by HPLC-MS. Simultaneously, the expression levels of glucosinolate biosynthesis pathway genes from four varieties with high and low total glucosinolate contents were analyzed by the qRT-PCR method. Four types of aliphatic glucosinolates (A-GLSs: GRA, SIN, GNA and GER) and indole glucosinolates (I-GLSs: 4-HGBS, GBS, 4-MGBS and NGBS) were detected in the stems and leaves of 16 varieties, and no aromatic glucosinolates (R-GLSs) were detected. A-GLSs account for more than 80.69% of the total content of total glucosinolates (T-GLSs), in which GNA and GRA are the main components of stems and leaves. Among Japanese varieties, QB1 has higher content of A- and T-GLSs, while that of XLB was lower; however, the corresponding varieties were ZH and DSHH in Chinese varieties. Among the above four varieties, the expression levels of SOT16, CYP83B1, SOT17, CYP83A1 and MAM1 genes were significantly higher in the varieties with higher GLSs; the expression levels of SOT16 and CYP83B1 were consistent with the content of I-GLSs; and SOT17, CYP83A1 and MAM1 expression levels were consistent with A-GLSs content. At the same time, the expression levels of SOT16 and CYP83B1 in the leaves were higher than those in the stems. CYP83A1 and MAM1 genes were less expressed in the leaves than in the stems of lower content varieties. It is speculated that these genes may be the key genes regulating GLS biosynthesis in Chinese kale.