bluuebunny/crossref_metadata_2025 huggingface dataset:
Withania somnifera and Centella asiatica Extracts Ameliorate Behavioral Deficits in an In Vivo Drosophila melanogaster Model of Oxidative Stress
Authors: Cabey Kadine , Long Dani M. , Law Alexander , Gray Nora E. , McClure Christine , Caruso Maya , Lak Parnian , Wright Kirsten M. , Stevens Jan F. , Maier Claudia S. , Soumyanath Amala , Kretzschmar Doris
Published: January 2022

Due to an increase in the aging population, age-related diseases and age-related changes, such as diminished cognition and sleep disturbances, are an increasing health threat. It has been suggested that an increase in oxidative stress underlies many of these changes. Current treatments for these diseases and changes either have low efficacy or have deleterious side effects preventing long-time use. Therefore, alternative treatments that promote healthy aging and provide resilience against these health threats are needed. The herbs Withania somnifera and Centella asiatica may be two such alternatives because both have been connected with reducing oxidative stress and could therefore ameliorate age-related impairments. To test the effects of these herbs on behavioral phenotypes induced by oxidative stress, we used the Drosophila melanogaster sniffer mutant which has high levels of oxidative stress due to reduced carbonyl reductase activity. Effects on cognition and mobility were assessed using phototaxis assays and both, W. somnifera and C. asiatica water extracts improved phototaxis in sniffer mutants. In addition, W. somnifera improved nighttime sleep in male and female sniffer flies and promoted a less fragmented sleep pattern in male sniffer flies. This suggests that W. somnifera and C. asiatica can ameliorate oxidative stress-related changes in behavior and that by doing so they might promote healthy aging in humans.
Modeling and Validation of a Passive Truss-Link Mechanism for Deployable Structures Considering Friction Compensation with Response Surface Methods
Authors: Choi Han-Sol , Kim Dong-Yeon , Park Jeong-Hoon , Lim Jae Hyuk , Jang Tae Seong
Published: January 2022

In this study, a passive truss-link mechanism applicable to large-scale deployable structures was designed to achieve successful deployment in space. First, we simplified the selected truss-link mechanisms to the two-dimensional geometry and calculated the degrees of freedom (DOF) to determine whether a kinematic over-constraint occurs. The dimensions of the truss-link structure were determined through a deployment kinematic analysis. Second, a deployment simulation with the truss-link was conducted using multibody dynamics (MBD) software. Finally, a deployment test was performed considering gravity compensation, and the results were compared with those of MBD simulation. The results of the deployment simulations were confirmed to be slightly faster than those of the deployment test due to friction effects existing in the joints and gravity compensation devices. To address this issue, inverse identification of the equivalent frictional torque (EFT) at the revolute joints in the deployment test was conducted through response surface methods (RSM) combined with the central composite design technique. As a result, we confirmed that the deployment angle history of the deployment simulation was similar to that of the deployment test.
Research on Tracking the Behavior of the Ciobanus Forest Road over a Season Time through Specific Tests and Analysis
Authors: Bitir Ioan , Derczeni Rudolf , Lunguleasa Aurel , Spirchez Cosmin , Ciobanu Valentina
Published: January 2022

Forest roads are of great economic importance as they ensure the transport of logs and forest biomass toward collection and processing centers, which is why they should be evaluated periodically, in order to establish the degree of degradation and periodicity and rehabilitation methodology and procedures. The main purpose of the paper is to follow the behavior of the Ciobanus forest road through specific tests over a difficult season of 5 months, in order to diagnose the degree of surface wear and structure degradation. Regarding the traffic on this forest road, an exhaustive study was made during the 2013–2017 period, and for in situ or in laboratory tests a more complex study during the year 2018, in the March-June period was also made. Out of the total of 20 tests that evaluated the Ciobanus forest road, 5 of them were classified as appropriate and 15 unsuitable for traffic, meaning the forest road had to be completely rehabilitated. Moreover, it has been shown that this forest road is part of the category of secondary forest roads and needs a total overhaul to cope with the increasing traffic or tonnage of trucks. Through the methodology and the obtained results, the paper supports the specialists in the field of forest roads to be able to diagnose or evaluate such a road, and to realize a program and its timing for maintenance.
Structural Design and Optimization of Herringbone Grooved Journal Bearings Considering Turbulent
Authors: Liu Xichun , Chen Wei
Published: January 2022

In order to improve the performance of the traditional constant-width herringbone grooved journal bearing in a computed tomography tube under a high-temperature environment, the present study designed a convergent herringbone grooved journal bearing (HGJB) structure lubricated by liquid metal. The bearing oil film thickness and the Reynolds equation considering the influence of turbulence are established and solved by using the finite difference method in the oblique coordinate system. The performance of the two bearings was compared, and the static and dynamic performance change trends of the two bearing structures under different eccentricities were systematically studied. The results show that the convergent herringbone grooved journal bearings are superior to the constant-width herringbone grooved journal bearings in terms of bearing capacity and stiffness coefficient. At the same time, the influence of structural parameters, such as the number of grooves, helix angle, groove to ridge ratio, groove depth on the performance of the constant-width herringbone grooved journal bearings, and the convergent herringbone grooved journal bearings was studied. Finally, we conclude that the performance of the convergent herringbone grooved journal bearings is optimal when the number of grooves is 15–20, the helix angle is 30°–45°, the ratio of the groove to ridge is 1, and the groove depth is 0.02 mm –0.024 mm. This research has provided the thinking and reference basis for the design of liquid metal bearings for high-performance CT equipment.
Universal Dental Adhesives: Cost-Effectiveness and Duration of Use
Authors: Banjar Ayman A. , Nassar Hani M.
Published: January 2022

The purpose of this study was to conduct a cost-effectiveness analysis (CEA) of different brands of universal dental adhesives used for composite restorations. Four adhesive brands were included: Single-Bond Universal (SB), Tetric N-Bond Universal VivaPen (TN), OptiBond All-In-One (OB), and G-Premio Bond (GP). Adhesives were applied 5 times daily in a standardized class II cavity onto a plastic tooth. A precision-analysis scale was used to measure all of the following parameters before and after use: adhesive bottle, applicator, dosing plate, and plastic tooth. CEA was done by measuring the amount of material utilized/day, waste/day, efficacy, efficiency, average cost-effectiveness ratio (ACER), and incremental cost-effectiveness ratio (ICER). Data were analyzed using Kruskal–Wallis and Dunn’s tests with Bonferroni correction at 0.05 significance level. CEA tested parameters were significantly different between groups (p < 0.001) except for ICER (p = 0.112). GP was the least effective (median = 0.062), and SB was the least efficient (median = 0.366). The highest and lowest ACER values were associated with TN (median cost ≈ USD 317) and SB (median cost ≈ USD 317), respectively. ICER analysis reported an incremental cost for extra material utilized per milliliter of ≈USD 208 for TN, USD 3.8 for GP, and USD −38 for OB, compared to SB. TN seems to be the most efficient and cost-effective dental adhesive.
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
Authors: Sboev Alexander , Sboeva Sanna , Moloshnikov Ivan , Gryaznov Artem , Rybka Roman , Naumov Alexander , Selivanov Anton , Rylkov Gleb , Ilyin Vyacheslav
Published: January 2022

The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora.
Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework
Authors: Pandit Mahesha , Gupta Deepali , Anand Divya , Goyal Nitin , Aljahdali Hani Moaiteq , Mansilla Arturo Ortega , Kadry Seifedine , Kumar Arun
Published: January 2022

Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing.
Fundamental Study on Underwater Cutting of 50 mm-Thick Stainless Steel Plates Using a Fiber Laser for Nuclear Decommissioning
Authors: Kim Kwan , Song Moo-Keun , Lee Su-Jin , Shin Dongsig , Suh Jeong , Kim Jong-Do
Published: January 2022

With nuclear power plants worldwide approaching their design lifespans, plans for decommissioning nuclear power plants are increasing, and interest in decommissioning technology is growing. Laser cutting, which is suitable for high-speed cutting in underwater environments and is amenable to remote control and automation, has attracted considerable interest. In this study, the effects of laser cutting were analyzed with respect to relevant parameters to achieve high-quality underwater laser cutting for the decommissioning of nuclear power plants. The kerf width, drag line, and roughness of the specimens during the high-power laser cutting of 50 mm-thick stainless steel in an underwater environment were analyzed based on key parameters (focal position, laser power, and cutting speed) to determine the conditions for satisfactory cutting surface quality. The results indicated that underwater laser cutting with a speed of up to 130 mm/min was possible at a focal position of 30 mm and a laser power of 9 kW; however, the best-quality cutting surface was obtained at a cutting speed of 30 mm/min.
Thermal Image Generation for Robust Face Recognition
Authors: Pavez Vicente , Hermosilla Gabriel , Pizarro Francisco , Fingerhuth Sebastián , Yunge Daniel
Published: January 2022

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.
Silicon Ribbon-Based Dual-Beam Optical Phased Array with Low Crosstalk and Large FoV
Authors: Chi Hsun-Yuan , Hong Wei , Lai Hsuan , Hsu Shih-Hsiang , Chang Che-Chang
Published: January 2022

In this paper, a silicon ribbon (SR)-based microstructure is developed and added to a 32-channel optical phased array (OPA) to reduce the crosstalk between the antennas of grating waveguides. The spacing between the chirped grating antennas can be as close as 600 nm to effectively improve the field of view (FoV) of the OPA in the horizontal direction to 95 degrees. This SR-based approach substantially reduces the side lobe by 10 dB, effectively suppressing the noise and increasing the main lobe by 6 dB and considerably expanding the grating length with linear energy decay. The full width at the half maximum of the light spot reaches about 0.24 degrees. The antenna sites can simultaneously be scanned vertically by bi-directional inputs, effectively increasing the FoV to 30 degrees in the vertical direction.
Study of 222−220Rn Measurement Systems Based on Electrostatic Collection by Using Geant4+COMSOL Simulation
Authors: Rinaldi Luigi , Ambrosino Fabrizio , Roca Vincenzo , D’Onofrio Antonio , Sabbarese Carlo
Published: January 2022

Using Monte Carlo (with Geant4) and COMSOL simulations, the authors have defined a useful tool to reproduce the alpha spectroscopy of 222Rn, 220Rn and their ionized daughters by measurement systems based on electrostatic collection on a silicon detector, inside a metallic chamber. Several applications have been performed: (i) simulating commercial devices worldwide used, and comparing them with experimental theoretical results; (ii) studying of realization of new measurement systems through investigation of the detection efficiency versus different chamber geometries. New considerations and steps forward have been drawn. The present work is a novelty in the literature concerning this research framework.
An Evaluation of the Accuracy and Precision of Jump Height Measurements Using Different Technologies and Analytical Methods
Authors: Conceição Filipe , Lewis Martin , Lopes Hernâni , Fonseca Elza M. M.
Published: January 2022

This study aims to comprehensively assess the accuracy and precision of five different devices and by incorporating a variety of analytical approaches for measuring countermovement jump height: Qualisys motion system; Force platform; Ergojump; an Accelerometer, and self-made Abalakow jump belt. Twenty-seven male and female physical education students (23.5 ± 3.8 years; height 170 ± 9.1 cm and body mass 69.1 ± 11.4 kg) performed three countermovement jumps simultaneously measured using five devices. The 3D measured displacement obtained through the Qualisys device was considered in this study as the reference value. The best accuracy (difference from 3D measured displacement) and precision (standard deviation of differences) for countermovement jump measurement was found using the Abalakow jump belt (0.8 ± 14.7 mm); followed by the Force platform when employing a double integration method (1.5 ± 13.9 mm) and a flight-time method employed using Qualisys motion system data (6.1 ± 17.1 mm). The least accuracy was obtained for the Ergojump (−72.9 mm) employing its analytical tools and then for the accelerometer and Force platform using flight time approximations (−52.8 mm and −45.3 mm, respectively). The worst precision (±122.7 mm) was obtained through double integration of accelerometer acceleration data. This study demonstrated that jump height measurement accuracy is both device and analytical-approach-dependent and that accuracy and precision in jump height measurement are achievable with simple, inexpensive equipment such as the Abalakow jump belt.
Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education
Authors: Nawaz Raheel , Sun Quanbin , Shardlow Matthew , Kontonatsios Georgios , Aljohani Naif R. , Visvizi Anna , Hassan Saeed-Ul
Published: January 2022

Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning.
In Silico Studies of Tumor Targeted Peptide-Conjugated Natural Products for Targeting Over-Expressed Receptors in Breast Cancer Cells Using Molecular Docking, Molecular Dynamics and MMGBSA Calculations
Authors: Hart Lucy R. , Lebedenko Charlotta G. , Mitchell Saige M. , Daso Rachel E. , Banerjee Ipsita A.
Published: January 2022

In this work, in silico studies were carried out for the design of diterpene and polyphenol-peptide conjugates to potentially target over-expressed breast tumor cell receptors. Four point mutations were induced into the known tumor-targeting peptide sequence YHWYGYTPQN at positions 1, 2, 8 and 10, resulting in four mutated peptides. Each peptide was separately conjugated with either chlorogenate, carnosate, gallate, or rosmarinate given their known anti-tumor activities, creating dual targeting compounds. Molecular docking studies were conducted with the epidermal growth factor receptor (EGFR), to which the original peptide sequence is known to bind, as well as the estrogen receptor (ERα) and peroxisome proliferator-activated receptor (PPARα) using both Autodock Vina and FireDock. Based on docking results, peptide conjugates and peptides were selected and subjected to molecular dynamics simulations. MMGBSA calculations were used to further probe the binding energies. ADME studies revealed that the compounds were not CYP substrates, though most were Pgp substrates. Additionally, most of the peptides and conjugates showed MDCK permeability. Our results indicated that several of the peptide conjugates enhanced binding interactions with the receptors and resulted in stable receptor-ligand complexes; Furthermore, they may successfully target ERα and PPARα in addition to EGFR and may be further explored for synthesis and biological studies for therapeutic applications.
DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion
Authors: Zhao Na , Liu Qian , Jing Ming , Li Jie , Zhao Zhidan , Wang Jian
Published: January 2022

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.
Effect of Rice Straw on Tensile Properties of Tailings Cemented Paste Backfill
Authors: Li Zeyu , Shi Xiuzhi , Chen Xin
Published: January 2022

It is important and difficult to improve the tensile strength of backfill material to ensure the stability of goafs. In this study, rice straw (RS) in fiber form is used to improve the tensile properties of cemented paste backfill (CPB). An orthogonal experiment was designed, Brazilian indirect tensile strength tests were conducted to test the tensile performance of RS fiber-reinforced cemented paste backfill (RSCPB) under different fiber content (1, 2, 3 kg/m3) and fiber length (0.8~1, 1~3, 3~5 cm), and the microstructure of RSCPB was analyzed with scanning electron microscopy (SEM). The results showed that, compared with the conventional cemented paste backfill (CCPB), the increase in tensile strength of RSCPB ranged from 115.38% to 300.00% at 3 days curing age, 40.91% to 346.15% at 7 days, and −38.10% to 28.00% at 28 days, and the strain was slightly reduced during the curing period. The tensile strength, strain, and percentage increase of the RSCPB compared to the CCBP did not show a monotonic pattern of variation with the RS fiber content and length during the curing period. The RSCPB samples fractured under peak stress, showing obvious brittle failure. In addition, sulfate generated from S2− in the tailings inhibits the hydration reaction, and generates swelling products that form weak structural surfaces, which, in turn, lead to a 28-day tensile strength and strain of RSCPB lower than those at 7 days.
Recovery of Haemal Lordosis in European Seabass Dicentrarchus labrax (Linnaeus 1758)
Authors: Fragkoulis Stephanos , Kourkouta Chara , Geladakis George , Printzi Alice , Glaropoulos Alexis , Koumoundouros George
Published: January 2022

Lordosis of the haemal part of the vertebral column is a frequent abnormality in reared fish. Haemal lordosis develops during the late larval and early juvenile life stages of fish, mainly due to the high swimming activity of the fish in the rearing tanks. In the present study, we have examined whether haemal lordosis recovers during the growth of European seabass. Furthermore, we aimed to develop simple morphometric indices (PrAn1 and PrAn2) that might link the severity of lordosis at the juvenile stage with fish morphological quality at harvesting. At 111 days post-hatching (dph, 53 ± 4 mm standard length, SL), 600 seabass juveniles with lordotic (L, 200 fish) or normal (N, 400 fish) external morphology were selected and introduced in a common tank. At 150 dph (75 ± 7 mm SL), 350 fish were randomly selected, pit-tagged and transferred to a sea cage for on-growing up to 502 dph (234 ± 16 mm SL). The morphological examination of the fish at 150 and 502 dph revealed that 60% (46 out of 77) of L juveniles turned into normal phenotype by the end of on-growing period. Interestingly, 56% of the fish with recovered external morphology (N-Rec) presented either a completely normal vertebral column (31%) or minor abnormalities of individual vertebrae (25%). Following the results of geometric morphometric analysis, the differences in the body shape between N-Rec and N fish were not statistically significant (p > 0.05, canonical variate analysis). The examined morphometric indices were effective in discriminating the normal fish from 58% (PrAn1) to 65% (PrAn2) of lordotic juveniles. Results are discussed with respect to the mechanism of lordosis recovery, and spotlights on their application for quality control and cull out of the abnormal fish in commercial hatcheries.
The Architectural Typology of Contemporary Façades for Public Buildings in the European Context
Authors: Jabłońska Joanna , Telesińska Małgorzata , Adamska Agnieszka , Gronostajska Joanna
Published: January 2022

In contemporary architecture, a border between an exterior and an interior—a façade—is variously designed in terms of form, style, response to climate or culture, individual approach or tools used. Despite the diversity and multi-tread theoretical and practical discourse, the Authors propose the typology of contemporary façades for public buildings (open to society) in the context of European cities by extracting comprehensive architectural features. The term systematic reflects the complexness of the issue by the newly proposed element. Namely, it is a representation of a particular architectural feature with the use of scale. The elaboration consists of (1) an introduction with a literature review and thesis, (2) our aim and method, (3) a historical background; case studies, and systematics introduction (4) conclusions with typology proposal.
Offensive-Language Detection on Multi-Semantic Fusion Based on Data Augmentation
Authors: Liu Junjie , Yang Yong , Fan Xiaochao , Ren Ge , Yang Liang , Ning Qian
Published: January 2022

The rapid identification of offensive language in social media is of great significance for preventing viral spread and reducing the spread of malicious information, such as cyberbullying and content related to self-harm. In existing research, the public datasets of offensive language are small; the label quality is uneven; and the performance of the pre-trained models is not satisfactory. To overcome these problems, we proposed a multi-semantic fusion model based on data augmentation (MSF). Data augmentation was carried out by back translation so that it reduced the impact of too-small datasets on performance. At the same time, we used a novel fusion mechanism that combines word-level semantic features and n-grams character features. The experimental results on the two datasets showed that the model proposed in this study can effectively extract the semantic information of offensive language and achieve state-of-the-art performance on both datasets.
Effect of Production Technique on Pilsner-Style Non-Alcoholic Beer (NAB) Chemistry and Flavor
Authors: Rettberg Nils , Lafontaine Scott , Schubert Christian , Dennenlöhr Johanna , Knoke Laura , Diniz Fischer Patrícia , Fuchs Johannes , Thörner Sarah
Published: January 2022

The sensory, volatile, and physiochemical profiles of nineteen commercial non-alcoholic pilsner-style beers produced by different production techniques were analyzed and compared with a dry-hopped non-alcoholic IPA. NABs made only with either physical dealcoholization or restricted fermentations differed significantly in chemistry and flavor. Generally, NABs produced by restricted fermentations were the most worty, thick, and sweet, whereas NABs that were physically dealcoholized had the lowest taste/aroma intensities and were the sourest, most thin, and least sweet. Interestingly, the method of dealcoholization had a minor impact on the flavor profile. The use of maltose intolerant yeast as well as the implementation of combined treatments, such as blending dealcoholized beer with beer containing alcohol, were the techniques found to produce NABs with more harmonious and multifaceted chemical and flavor profiles. NABs with increased hop aroma volatiles were the most harmonious, particularly highlighted by the NA IPA reference. Even though dry-hopped character might be atypical for pilsner-style beer, dry-hopping appears as a simple application to produce NABs with more harmonious flavor.
Resolution of Inflammation in Acute Graft-Versus-Host-Disease: Advances and Perspectives
Authors: Duarte Layara Roberta Ferreira , Pinho Vanessa , Rezende Barbara Maximino , Teixeira Mauro Martins
Published: January 2022

Inflammation is an essential reaction of the immune system to infections and sterile tissue injury. However, uncontrolled or unresolved inflammation can cause tissue damage and contribute to the pathogenesis of various inflammatory diseases. Resolution of inflammation is driven by endogenous molecules, known as pro-resolving mediators, that contribute to dampening inflammatory responses, promoting the resolution of inflammation and the recovery of tissue homeostasis. These mediators have been shown to be useful to decrease inflammatory responses and tissue damage in various models of inflammatory diseases. Graft-versus-host disease (GVHD) is a major unwanted reaction following allogeneic hematopoietic stem cell transplantation (allo-HSCT) and is characterized by an exacerbated inflammatory response provoked by antigen disparities between transplant recipient and donor. There is no fully effective treatment or prophylaxis for GVHD. This review explores the effects of several pro-resolving mediators and discusses their potential use as novel therapies in the context of GVHD.
Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data
Authors: Chatterjee Avishek , Wilmink Guus , Woodruff Henry , Lambin Philippe
Published: January 2022

We conducted a systematic survey of COVID-19 endpoint prediction literature to: (a) identify publications that include data that adhere to FAIR (findability, accessibility, interoperability, and reusability) principles and (b) develop and reuse mortality prediction models that best generalize to these datasets. The largest such cohort data we knew of was used for model development. The associated published prediction model was subjected to recursive feature elimination to find a minimal logistic regression model which had statistically and clinically indistinguishable predictive performance. This model could still not be applied to the four external validation sets that were identified, due to complete absence of needed model features in some external sets. Thus, a generalizable model (GM) was built which could be applied to all four external validation sets. An age-only model was used as a benchmark, as it is the simplest, effective, and robust predictor of mortality currently known in COVID-19 literature. While the GM surpassed the age-only model in three external cohorts, for the fourth external cohort, there was no statistically significant difference. This study underscores: (1) the paucity of FAIR data being shared by researchers despite the glut of COVID-19 prediction models and (2) the difficulty of creating any model that consistently outperforms an age-only model due to the cohort diversity of available datasets.
Autoantibody to GNAS in Early Detection of Hepatocellular Carcinoma: A Large-Scale Sample Study Combined with Verification in Serial Sera from HCC Patients
Authors: Wang Xiao , Wang Keyan , Qiu Cuipeng , Wang Bofei , Zhang Xiaojun , Ma Yangcheng , Dai Liping , Zhang Jian-Ying
Published: January 2022

The aim of this study was to explore the value of autoantibody to GNAS in the early detection of hepatocellular carcinoma (HCC). In a large-scale sample set of 912 participants (228 cases in each of HCC, liver cirrhosis (LC), chronic hepatitis B (CHB), and normal controls (NCs) groups), autoantibody to GNAS was detected with a positive result in 47.8% of HCC patients, which was significantly higher than that in patients with LC (35.1%), CHB (19.7%), and NCs (19.7%). Further analysis showed that the frequency of autoantibody to GNAS started increasing in compensated cirrhosis patients (37.0%) with a jump in decompensated cirrhosis patients (53.2%) and reached a peak in early HCC patients (62.4%). The increasing autoantibody response to GNAS in patients at different stages was closely associated with the progression of chronic liver lesions. The result from 44 human serial sera demonstrated that 5 of 11 (45.5%) HCC patients had elevated autoantibody to GNAS before and/or at diagnosis of HCC. Moreover, 46.1% and 62.4% of high positive rates in alpha-fetoprotein (AFP) negative and early-stage HCC patients can supplement AFP in early detection of HCC. These findings suggest that autoantibody to GNAS could be used as a potential biomarker for the early detection of HCC.
Antioxidants as Therapeutic Agents in Acute Respiratory Distress Syndrome (ARDS) Treatment—From Mice to Men
Authors: von Knethen Andreas , Heinicke Ulrike , Laux Volker , Parnham Michael J. , Steinbicker Andrea U. , Zacharowski Kai
Published: January 2022

Acute respiratory distress syndrome (ARDS) is a major cause of patient mortality in intensive care units (ICUs) worldwide. Considering that no causative treatment but only symptomatic care is available, it is obvious that there is a high unmet medical need for a new therapeutic concept. One reason for a missing etiologic therapy strategy is the multifactorial origin of ARDS, which leads to a large heterogeneity of patients. This review summarizes the various kinds of ARDS onset with a special focus on the role of reactive oxygen species (ROS), which are generally linked to ARDS development and progression. Taking a closer look at the data which already have been established in mouse models, this review finally proposes the translation of these results on successful antioxidant use in a personalized approach to the ICU patient as a potential adjuvant to standard ARDS treatment.
Non-Integrating Lentiviral Vectors in Clinical Applications: A Glance Through
Authors: Gurumoorthy Narmatha , Nordin Fazlina , Tye Gee Jun , Wan Kamarul Zaman Wan Safwani , Ng Min Hwei
Published: January 2022

Lentiviral vectors (LVs) play an important role in gene therapy and have proven successful in clinical trials. LVs are capable of integrating specific genetic materials into the target cells and allow for long-term expression of the cDNA of interest. The use of non-integrating LVs (NILVs) reduces insertional mutagenesis and the risk of malignant cell transformation over integrating lentiviral vectors. NILVs enable transient expression or sustained episomal expression, especially in non-dividing cells. Important modifications have been made to the basic human immunodeficiency virus (HIV) structures to improve the safety and efficacy of LVs. NILV-aided transient expression has led to more pre-clinical studies on primary immunodeficiencies, cytotoxic cancer therapies, and hemoglobinopathies. Recently, the third generation of self-inactivating LVs was applied in clinical trials for recombinant protein production, vaccines, gene therapy, cell imaging, and induced pluripotent stem cell (iPSC) generation. This review discusses the basic lentiviral biology and the four systems used for generating NILV designs. Mutations or modifications in LVs and their safety are addressed with reference to pre-clinical studies. The detailed application of NILVs in promising pre-clinical studies is also discussed.
Macrophages Modulate Hepatic Injury Involving NLRP3 Inflammasome: The Example of Efavirenz
Authors: Alegre Fernando , Martí-Rodrigo Alberto , Polo Miriam , Ortiz-Masiá Dolores , Bañuls Celia , Pinti Marcello , Álvarez Ángeles , Apostolova Nadezda , Esplugues Juan V. , Blas-García Ana
Published: January 2022

Drug-induced liver injury (DILI) constitutes a clinical challenge due to the incomplete characterization of the mechanisms involved and potential risk factors. Efavirenz, an anti-HIV drug, induces deleterious actions in hepatocytes that could underlie induction of the NLRP3 inflammasome, an important regulator of inflammatory responses during liver injury. We assessed the potential of efavirenz to modulate the inflammatory and fibrogenic responses of major liver cell types involved in DILI. The effects of efavirenz were evaluated both in vitro and in vivo. Efavirenz triggered inflammation in hepatocytes, in a process that involved NF-κB and the NLRP3 inflammasome, and activated hepatic stellate cells (HSCs), thereby enhancing expression of inflammatory and fibrogenic markers. The NLRP3 inflammasome was not altered in efavirenz-treated macrophages, but these cells polarized towards the anti-inflammatory M2 phenotype and displayed upregulated anti-inflammatory mediators. Conversely, no evidence of damage was observed in efavirenz-treated animals, except when macrophages were depleted, which resulted in the in vivo manifestation of the deleterious effects detected in hepatocytes and HSCs. Efavirenz elicits a cell-specific activation of the NLRP3 inflammasome in hepatocytes and HSCs, but macrophages appear to counteract efavirenz-induced liver injury. Our results highlight the dynamic nature of the interaction among liver cell populations and emphasize the potential of targeting macrophage polarization as a strategy to treat NLRP3 inflammasome-induced liver injury.
RETRACTED: Constantin et al. Animal Models in Bladder Cancer. Biomedicines 2021, 9, 1762
Authors: Constantin Traian , Păvălean Mihai , Bucur Ștefana , Constantin Maria , Nicolescu Alin , Pacu Irina , Mădan Victor
Published: January 2022

The journal retracts the article, “Animal Models in Bladder Cancer. Biomedicines 2021, 9, 1762” [...]
Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model
Authors: Mohd Noor Nor Safira Elaina , Ibrahim Haidi , Che Lah Muhammad Hanif , Abdullah Jafri Malin
Published: January 2022

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.
Relationship between Breast Cancer Surgical Treatment and Psychiatric Symptomatology: Which Sociodemographic and Clinical Factors Could Influence It? A Preliminary Study
Authors: Baldelli Ilaria , Gari Matteo , Aguglia Andrea , Amerio Andrea , Berrino Valeria , Santori Gregorio , Friedman Daniele , Serafini Gianluca , Amore Mario , Raposio Edoardo
Published: January 2022

This study aimed to investigate psychiatric symptomatology in a sample of patients affected by breast cancer undergoing surgery, evaluating the potential mediators on perceived stress levels, depression and hopelessness. The study was conducted on eighty-five patients with breast cancer, admitted consecutively to the Breast Unit of the IRCCS Ospedale Policlinico San Martino, between May 2018 and December 2019. Sociodemographic (age of diagnosis, gender, marital and occupational status, educational level, having children) and clinical (type and side of surgery, previous breast surgery, neoadjuvant chemotherapy and axillary dissection) characteristics were investigated through a semi-structured interview. The following rating scales were administered: Beck Depression Inventory, Beck Hopelessness Scale, and Perceived Stress Scale. Our findings indicate that the presence of children and of a partner was associated with a lower total score on the clinical dimensions evaluated. Furthermore, we found demolitive surgery to be a mediator between perceived stress and hopelessness, while history of previous breast surgery was found to be a mediator between demolitive surgery and perceived stress. In conclusion, patients affected by breast cancer undergoing more complex and demolitive surgery or with history of previous breast surgery should be mostly monitored from a psychological and psychiatric point of view from the beginning of treatments to evaluate the first manifestations of psychiatric symptomatology.
Specific Skill Requirements within Prefabricated Residential Construction: Stakeholders’ Perspectives
Authors: Rotimi Funmilayo Ebun , Almughrabi Firas Majthoub , Samarasinghe Don Amila Sajeevan , Silva Chathurani
Published: January 2022

Skill availability is an important component in the uptake of prefabrication and plays a crucial role in housing supply. However, the challenge is that the demand for housing has outgrown the availability of specifically trained workers. This challenge is not unique to New Zealand; many developed countries worldwide are facing similar issues. Therefore, the purpose of this study is to determine relevant skills in the prefabricated residential construction sector in New Zealand (NZ) and suggest improvement measures from the standpoint of industry stakeholders. The study adopted a semi-structured online survey and administered it to multiple construction industry practitioners. The study found the training of the construction workforce as one significant area of focus. In addition, external sourcing of international prefabrication-specific skilled workers could improve the issues of skill shortages in the residential prefabrication sector. Furthermore, the study revealed that the barriers to healthier prefabrication uptake are closely linked to shortages in management, digital architecture and design, and vocational skills related to residential construction. The study has contributed to the current pool of knowledge by identifying skill issues in NZ’s prefabricated residential construction sector, classifying the major restraints limiting prefabrication implementation, and determining measures for increasing industry uptake. It is anticipated that this will help construction organizations and the wider industry develop strategic goals and a roadmap for meeting the skill requirements in NZ. Training policies and programmes can be developed with focus on crucial prefabrication skill requirements at governmental level. Curriculum reviews are recommended for uptake by academic and vocational institutions.
Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods
Authors: Ejohwomu Obuks Augustine , Shamsideen Oshodi Olakekan , Oladokun Majeed , Bukoye Oyegoke Teslim , Emekwuru Nwabueze , Sotunbo Adegboyega , Adenuga Olumide
Published: January 2022

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.