bluuebunny/crossref_metadata_2025 huggingface dataset:
Research and Challenges of Reinforcement Learning in Cyber Defense Decision-Making for Intranet Security
Authors: Wang Wenhao , Sun Dingyuanhao , Jiang Feng , Chen Xingguo , Zhu Cheng
Published: April 2022

In recent years, cyber attacks have shown diversified, purposeful, and organized characteristics, which pose significant challenges to cyber defense decision-making on internal networks. Due to the continuous confrontation between attackers and defenders, only using data-based statistical or supervised learning methods cannot cope with increasingly severe security threats. It is urgent to rethink network defense from the perspective of decision-making, and prepare for every possible situation. Reinforcement learning has made great breakthroughs in addressing complicated decision-making problems. We propose a framework that defines four modules based on the life cycle of threats: pentest, design, response, recovery. Our aims are to clarify the problem boundary of network defense decision-making problems, to study the problem characteristics in different contexts, to compare the strengths and weaknesses of existing research, and to identify promising challenges for future work. Our work provides a systematic view for understanding and solving decision-making problems in the application of reinforcement learning to cyber defense.
Heterogeneous Multi-Agent-Based Fault Diagnosis Scheme for Actuation System
Authors: Cao Yuyan , Li Ting , Li Yang , Wang Xinmin
Published: April 2022

In this paper, a fault diagnosis method of a heterogeneous multi-agent is proposed that realizes the rapid and accurate fault diagnosis of a redundant multi-type actuation system of large aircraft. Firstly, the multi-agent model of a large aircraft actuation system is established, the composition of the actuation system and the relationship between each multi-agent are clarified and three different types of actuator mathematical models are established. Secondly, a fault detection and isolation (FDI) model is established and transformed into an optimization problem according to different performance index requirements. Aiming at the optimization problem, combined with the principle of linear matrix inequality (LMI), the fault diagnosis algorithm of a heterogeneous multi-agent system is designed. Moreover, the threshold judgment method based on the error signal is presented. Finally, the three actuator models of the aileron actuation system of large aircraft are combined to complete the fault diagnosis of a heterogeneous multi-agent system under the given model interference and model fault. The obtained results demonstrate and validate that the proposed method can accurately and effectively diagnose the faults of the actuator and its associated actuators.
Influence of the New Energy Context on the Spanish Agri-Food Industry
Authors: García José L. , Perdigones Alicia , Benavente Rosa M. , Mazarrón Fernando R.
Published: April 2022

The Spanish agri-food industries face a new energy context radically different from the one existing less than a year ago. A comparable situation is occurring in other European countries. The great variability and different seasonality of the agro-industries condition the adaptation measures to be implemented. This article quantifies the impact of the new energy context on real industries with quite different consumption patterns. More than 1000 scenarios have been analysed to determine the effectiveness of adaptation measures. The study results demonstrate the critical situation to which the sector is subjected. The new tariff system affects the agri-food sector in an unequal way, benefiting industries with marked seasonality. In just one year, the electricity bill has increased between 62% and 151%. The savings generated by implementing measures such as optimising contracted power to a new hourly discrimination system (up to 6%) or shifting consumption to lower-cost periods (up to 21%) do not seem sufficient. If prices do not fall drastically, immediate installation of generation systems, such as photovoltaic panels, appears essential to maintain the viability and competitiveness of the sector.
Comparative Analysis of Canopy Cooling in Wheat under High Temperature and Drought Stress
Authors: Thakur Vidisha , Rane Jagadish , Nankar Amol N.
Published: April 2022

The size and the weight of wheat grains vary across the length of each spike (Triticum aestivum L.). High temperature and water scarcity often reduce the single grain weight, and this reduction also varies across the spike length. Plants tend to cope with high temperature and drought stress through inherent mechanisms such ascanopy cooling through transpiration, which can contribute to yield stability. The effect of canopy cooling on the average grain weight at different positions in spike is still unknown. In this study, we planned to assess the role of canopy temperature, yield-related traits, and spike shape in final grain weight. For two years (2017–2018 and 2018–2019), fifteen diverse genotypes released for cultivation in different environmental conditions were grown in the field. They were examined for canopy temperature, spikelets spike−1, grain number spike−1, grain yield spike−1, and grain weight of the spike’s basal, median, and distal regions. The Pearson correlation coefficient (r) was obtained for all pair-wise combinations of traits under different treatments and spike shapes. The results indicated that cooler canopy is correlated to grain weight in normal spike shape at all three positions within the spike irrespective of stress. The advantage of the cooler canopy in improving grain-filling at basal, median, and distal regions was more conspicuous in the high temperature stress conditions compared to non-stressed and drought conditions.
Does Plant Breeding for Antioxidant-Rich Foods Have an Impact on Human Health?
Authors: Bassolino Laura , Petroni Katia , Polito Angela , Marinelli Alessandra , Azzini Elena , Ferrari Marika , Ficco Donatella B. M. , Mazzucotelli Elisabetta , Tondelli Alessandro , Fricano Agostino , Paris Roberta , García-Robles Inmaculada , Rausell Carolina , Real María Dolores , Pozzi Carlo Massimo , Mandolino Giuseppe , Habyarimana Ephrem , Cattivelli Luigi
Published: April 2022

Given the general beneficial effects of antioxidants-rich foods on human health and disease prevention, there is a continuous interest in plant secondary metabolites conferring attractive colors to fruits and grains and responsible, together with others, for nutraceutical properties. Cereals and Solanaceae are important components of the human diet, thus, they are the main targets for functional food development by exploitation of genetic resources and metabolic engineering. In this review, we focus on the impact of antioxidants-rich cereal and Solanaceae derived foods on human health by analyzing natural biodiversity and biotechnological strategies aiming at increasing the antioxidant level of grains and fruits, the impact of agronomic practices and food processing on antioxidant properties combined with a focus on the current state of pre-clinical and clinical studies. Despite the strong evidence in in vitro and animal studies supporting the beneficial effects of antioxidants-rich diets in preventing diseases, clinical studies are still not sufficient to prove the impact of antioxidant rich cereal and Solanaceae derived foods on human
Carotenoids: Dietary Sources, Extraction, Encapsulation, Bioavailability, and Health Benefits—A Review of Recent Advancements
Authors: Saini Ramesh Kumar , Prasad Parchuri , Lokesh Veeresh , Shang Xiaomin , Shin Juhyun , Keum Young-Soo , Lee Ji-Ho
Published: April 2022

Natural carotenoids (CARs), viz. β-carotene, lutein, astaxanthin, bixin, norbixin, capsanthin, lycopene, canthaxanthin, β-Apo-8-carotenal, zeaxanthin, and β-apo-8-carotenal-ester, are being studied as potential candidates in fields such as food, feed, nutraceuticals, and cosmeceuticals. CAR research is advancing in the following three major fields: (1) CAR production from natural sources and optimization of its downstream processing; (2) encapsulation for enhanced physical and chemical properties; and (3) preclinical, clinical, and epidemiological studies of CARs’ health benefits. This review critically discusses the recent developments in studies of the chemistry and antioxidant activity, marketing trends, dietary sources, extraction, bioaccessibility and bioavailability, encapsulation methods, dietary intake, and health benefits of CARs. Preclinical, clinical, and epidemiological studies on cancer, obesity, type 2 diabetes (T2D), cardiovascular diseases (CVD), osteoporosis, neurodegenerative disease, mental health, eye, and skin health are also discussed.
Two Novel Lipophilic Antioxidants Derivatized from Curcumin
Authors: Liu Tao , Liu Xiaohan , Olajide Tosin M. , Xu Jia , Weng Xinchu
Published: April 2022

Tert-butyl curcumin (TBC), demethylated tert-butylated curcumin (1E,6E-1,7-bis(3-tert-butyl-4,5-dihydroxyphenyl)hepta-1,6-diene-3,5-dione, DMTC), demethylated curcumin (DMC), and Cur were synthesized from the starting compound, 2-methoxy-4-methylphenol. TBC and DMTC are two novel lipophilic compounds, and Cur and DMC are polar and hydrophilic. The antioxidant activities of Cur, TBC, DMC, and DMTC were evaluated by using the methods of 2,2-diphenyl-1-(2,4,6-trinitro-phenyl)-hydrazinyl (DPPH), deep-frying, and Rancimat. Tert-butylhydroquinone (TBHQ) and Butylated hydroxytoluene (BHT) were used as comparison compounds. Both Rancimat and deep-frying tests demonstrated that DMTC was the strongest antioxidant, and TBC also had stronger antioxidant activity than Cur. In the DPPH assay, DMC showed the highest scavenging activity, followed by DMTC, TBHQ, Cur, and TBC. DMTC and TBC can be potentially used as strong antioxidants in food industry, especially for frying, baking, and other high temperature food processing. DMTC is the strongest antioxidant in oil to our knowledge.
Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach
Authors: AlGhamdi Najwa , Khatoon Shaheen , Alshamari Majed
Published: April 2022

User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.
Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence
Authors: Díaz Raymundo , Solares Efrain , de-León-Gómez Victor , Salas Francisco G.
Published: April 2022

Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of this work is two-fold: first, to describe an approach that comprehensively addresses the main activities carried out by practitioners during portfolio management (price forecasting, stock selection and portfolio optimization) and, second, to consider uptrends and downtrends in prices. Both aspects are relevant for practitioners but, to the best of our knowledge, the literature does not have an approach addressing them together. We propose to do it by exploiting various computational intelligence techniques. The assessment of the proposal shows that further improvements to the procedure are obtained when considering downtrends and that the procedure allows obtaining portfolios with better returns than those produced by the considered benchmarks. These results indicate that practitioners should consider the proposed procedure as a complement to their current methodologies in managing stock portfolios.
MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
Authors: Qu Shenming , Zhou Huafei , Zhang Bo , Liang Shengbin
Published: April 2022

Extracting roads from remote sensing images can support a range of geo-information applications. However, it is challenging due to factors such as the complex distribution of ground objects and occlusion of buildings, trees, shadows, etc. Pixel-wise classification often fails to predict road connectivity and thus produces fragmented road segments. In this paper, we propose a multi-scale strip pooling network (MSPNet) to learn the linear features of roads. Motivated by the strip pooling being more aligned with the shape of roads, which are long-span and narrow, we develop a multi-scale strip pooling (MSP) module that utilizes strip pooling layers with long but narrow kernel shapes to capture multi-scale long-range context from horizontal and vertical directions. The proposed MSP module focuses on establishing relationships along the road region to guarantee the connectivity of roads. Considering the complex distribution of ground objects, the spatial pyramid pooling is applied to enhance the learning ability of complex features in different sub-regions. In addition, to alleviate the problem caused by an imbalanced distribution of road and non-road pixels, we use binary cross-entropy and dice-coefficient loss functions to jointly train our proposed deep learning model. Then, we perform ablation experiments to adjust the loss contributions to suit the task of road extraction. Comparative experiments on a popular benchmark DeepGlobe dataset demonstrate that our proposed MSPNet establishes new competitive results in both IoU and F1-score.
Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel
Authors: Nguyen Thien An , Lee Jaejin
Published: April 2022

With a high capacity and fast data access rate, holographic data storage (HDS) is a potential candidate for future storage systems. However, for page-oriented data processing, two-dimensional (2D) interference appears intensely in the HDS systems. Therefore, the new 2D generalized partial response (GPR) target is introduced to estimate the 2D interference. In addition, we also propose a method to analyze the 2D GPR target into two serial one-dimensional (1D) GPR targets. It makes us design a simple detection scheme composed of two serial 1D detectors instead of a complicated 2D detector. In simulations, the results show that our proposed scheme can improve the BER performance compared to the conventional 1D GPR target model.
A New Method of Inland Water Ship Trajectory Prediction Based on Long Short-Term Memory Network Optimized by Genetic Algorithm
Authors: Qian Long , Zheng Yuanzhou , Li Lei , Ma Yong , Zhou Chunhui , Zhang Dongfang
Published: April 2022

Ship position prediction plays a key role in the early warning and safety of inland waters and maritime navigation. Ship pilots must have in-depth knowledge of the future position of their ship and target ship in a specific time period when maneuvering the ship to effectively avoid collisions. However, prediction accuracy and computing efficiency are crucial issues that need to be worked out at present. To solve these problems, in this paper, the deep long short-term memory network framework (LSTM) and genetic algorithm (GA) are introduced to predict the ship trajectory of inland water. Firstly, the collected actual automatic identification system (AIS) data are preprocessed and a series of typical trajectories are extracted from them; then, the LSTM network is used to predict the typical trajectories in real time. Considering that the hyperparameters of the LSTM network have difficulty obtaining the optimal solution manually, the GA is used to optimize hyperparameters of LSTM; finally, the GA-LSTM trajectory prediction model is constructed with the optimal network parameters and compared with the traditional support vector machine (SVM) model and LSTM model. The experimental results show that the GA-LSTM model effectively improves the accuracy and speed of trajectory prediction, with outstanding performance and good generalization, which possess certain reference values for the development of collision avoidance of unmanned ships.
One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
Authors: Xue Xiaozhou , Liang Qi , Gao Qunyu , Luo Zhigang
Published: April 2022

Cross-linked esterified starch (CES) was prepared using a one-step method, where maize starch was selected as the raw material, sodium trimetaphosphate as the cross-linking agent, and acetic anhydride as the esterifying agent, respectively. A response surface experiment was systematically conducted for analyzing the correlation of the experimental variables (cross-linked temperature, pH, reaction time, sodium trimetaphosphate and acetic anhydride dosage) and properties of the product (peak and final viscosity). The Brabender viscosity, freeze-thaw stability, shearing resistance, and acid tolerance of the cross-linked acetylated dual modified starch were studied under different conditions of crosslinking degree and acetyl content. Meanwhile, the granular structure and morphology of the modified starch were analyzed. The results indicated that: after cross-linked acetylated dual modification, the starch had a distinct birefringence and granular structure, along with the creation of new carbonyl groups. The low degree of crosslinking and high acetyl contents were beneficial to the viscosity, which was significantly increased at both low and high temperatures. Moreover, the freeze-thaw stability of CES was elevated sharply after five cycles. In addition, CES displayed increased shear and acid tolerance compared to the original waxy maize, and their lowest differences between waxy maize and CES were only 0.62% and 0.59%, respectively. In summary, a novel method for starch modification was provided, and the synthesized CES was suggested to have exceptional performance for the food industry.
A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions
Authors: Shahid Jahanzeb , Hameed Muhammad Khurram , Javed Ibrahim Tariq , Qureshi Kashif Naseer , Ali Moazam , Crespi Noel
Published: April 2022

The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many security issues that make these web applications vulnerable, thereby affecting the confidentiality, integrity, and availability of associated information systems. It has, therefore, become necessary to find vulnerabilities in these information system resources to guarantee information security. A publicly available web application vulnerability scanner is a computer program that assesses web application security by employing automated penetration testing techniques that reduce the time, cost, and resources required for web application penetration testing and eliminates test engineers’ dependency on human knowledge. However, these security scanners possess various weaknesses of not scanning complete web applications and generating wrong test results. Moreover, intensive research has been carried out to quantitatively enumerate web application security scanners’ results to inspect their effectiveness and limitations. However, the findings show no well-defined method or criteria available for assessing their results. In this research, we have evaluated the performance of web application vulnerability scanners by testing intentionally defined vulnerable applications and the level of their respective precision and accuracy. This was achieved by classifying the analyzed tools using the most common parameters. The evaluation is based on an extracted list of vulnerabilities from OWASP (Open Web Application Security Project).
Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset
Authors: Onuean Athita , Buatoom Uraiwan , Charoenporn Thatsanee , Kim Taehong , Jung Hanmin
Published: April 2022

In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai handwriting image dataset named Burapha-TH. The dataset has 68 character classes, 10 digit classes, and 320 syllable classes. For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets that were then digitized using a 300 dpi scanner. De-skewing, detection box and segmentation algorithms were applied to the raw scans for image extraction. The experiment used different deep convolutional models with the proposed dataset. The result shows that the VGG-13 model (with batch normalization) achieved accuracy rates of 95.00%, 98.29%, and 96.16% on character, digit, and syllable classes, respectively. The Burapha-TH dataset, unlike all other known Thai handwriting datasets, retains existing noise, the white background, and all artifacts generated by scanning. This comprehensive, raw, and more realistic dataset will be helpful for a variety of research purposes in the future.
The Role of Acetyl-Carnitine and Rehabilitation in the Management of Patients with Post-COVID Syndrome: Case-Control Study
Authors: Scaturro Dalila , Vitagliani Fabio , Di Bella Vito Emanuele , Falco Vincenzo , Tomasello Sofia , Lauricella Lorenza , Letizia Mauro Giulia
Published: April 2022

Post-COVID syndrome is characterized by the persistence of nonspecific disabling symptoms, even several months after the resolution of the infection, with clinical characteristics similar to fibromyalgia (FM) and a prevalence of 31%. We evaluated the effectiveness of physical exercise, in association with L-acetyl-carnitine (ALC) therapy, in patients with Post-COVID syndrome, on musculoskeletal pain, dyspnea, functional capacity, quality of life, and depression. We conducted an observational case-control study on patients with Post-COVID syndrome. The patients were randomly divided into two groups: a treatment group that received rehabilitation treatment in combination with ALC 500 mg therapy; a control group that received only rehabilitation treatment. Patients were assessed at the time of recruitment (T0) and one month after the end of therapy (T1), with the administration of rating scales: NRS, Barthel Dyspnea Index (NPI), 12-Item Short Form Survey (SF-12) scale, Fibromyalgia Impact Questionnaire (FIQ), and Patient Health Questionnaire (PHQ-9). The treatment group showed statistically higher variations in pain scores, quality of life, and depression. No statistically significant differences between the two groups emerged regarding changes in dyspnea and functional capacity scores. Combining exercise with ALC is a promising and effective treatment in the management of Post-COVID syndrome, especially for musculoskeletal pain, depression, and quality of life.
Editorial for Special Issue on Reliability Analysis of Electrotechnical Devices
Authors: Tan Cher-Ming
Published: April 2022

Advancement in electrotechnical devices has indeed revolutionize our daily lives [...]
Performance Index for in Home Assessment of Motion Abilities in Ataxia Telangiectasia: A Pilot Study
Authors: El Arayshi M. , Verrelli C. M. , Saggio G. , Iosa M. , Gentile A. E. , Chessa L. , Ruggieri M. , Polizzi A.
Published: April 2022

Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of the durations of specific walking gait subphases. Such harmonic proportions may be affected—partially or even totally destroyed—by several neurological and/or systemic disorders, thus drastically reducing the smooth, graceful, and melodic flow of movements and altering gait self-similarities. Aim. In this paper we aim at, preliminarily, showing the reliability of a technologically assisted methodology—performed with an easy to use wearable motion capture system—for the evaluation of motion abilities in Ataxia-Telangiectasia (AT), a rare infantile onset neurodegenerative disorder, whose typical neurological manifestations include progressive gait unbalance and the disturbance of motor coordination. Methods. Such an experimental methodology relies, for the first time, on the most recent accurate and objective outcome measures of gait recursivity and harmonicity and symmetry and double support subphase consistency, applied to three AT patients with different ranges of AT severity. Results. The quantification of the level of the distortions of harmonic temporal proportions is shown to include the qualitative evaluations of the three AT patients provided by clinicians. Conclusions. Easy to use wearable motion capture systems might be used to evaluate AT motion abilities through recursivity and harmonicity and symmetry (quantitative) outcome measures.
Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models
Authors: Cekikj Miodrag , Jakimovska Özdemir Milena , Kalajdzhiski Slobodan , Özcan Orhan , Sezerman Osman Uğur
Published: April 2022

Recent studies have highlighted that gut microbiota can alter colorectal cancer susceptibility and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology based on evaluating high-performance machine learning models where a Python-based random forest classifier provides the best performance metrics, with an overall accuracy of 91.7%. Our approach identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many studies point out the importance of present genera in the microbiome and intend to treat it separately. The symbiotic bacterial analysis generated different sets of joint feature combinations, providing a combined overview of the model’s predictiveness and uncovering additional data correlations where different genera joint impacts support the therapy-resistant effect. This study points out the different perspectives of treatment since our aggregate analysis gives precise results for the genera that are often found together in a resistant group of patients, meaning that resistance is not due to the presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that live in symbiosis.
Retrieval of Soil Moisture from FengYun-3D Microwave Radiation Imager Operational and Recalibrated Data Using Random Forest Regression
Authors: Wei Chuanwen , Weng Fuzhong , Wu Shengli , Wu Dongli , Zhang Peng
Published: April 2022

Three Microwave Radiation Imagers (MWRI) were carried onboard the FengYun-3B/C/D satellites and have collected more than 10 years of data since 2010. To create a robust climate quality of data, MWRI level one data were reprocessed with new calibration. This study evaluates the performance of retrieving global soil moisture from recalibrated MWRI data (RCD) and quantifies the difference of retrieved soil moisture between operational calibration data (OCD) and RCD. Soil Moisture Operational Products System (SMOPS) products from NOAA on four days of different seasons were collocated with MWRI brightness temperatures, and then the collocated data were used for training an algorithm through machine learning. The retrieved soil moisture products using OCD and RCD were evaluated against the independent SMOPS products, in situ networks and SMAP soil moisture product. It is shown that the algorithm from the random forest is suitable for FY-3D recalibrated MWRI data, with a coefficient of determination (R2) of 0.7223, a mean bias of −0.0062 and an unbiased root mean square difference (ubRMSD) of 0.0476 m3 m−3 compared with SMOPS products over the period from 12 July 2018 to 31 December 2019. The difference of retrieved soil moisture using OCD and RCD is spatially heterogeneous. Both temporal and spatial coverage and accuracy of the existing FY-3D operational soil moisture products are significantly improved.
HLA-A2 Promotes the Therapeutic Effect of Umbilical Cord Blood-Derived Mesenchymal Stem Cells in Hyperoxic Lung Injury
Authors: Kwak Jihye , Choi Wankyu , Bae Yunkyung , Kim Miyeon , Choi Soojin , Oh Wonil , Jin Hyejin
Published: April 2022

Mesenchymal stem cells (MSCs) are one of the most extensively studied stem cell types owing to their capacity for differentiation into multiple lineages as well as their ability to secrete regenerative factors and modulate immune functions. However, issues remain regarding their further application for cell therapy. Here, to demonstrate the superiority of the improvement of MSCs, we divided umbilical cord blood-derived MSCs (UCB-MSCs) from 15 donors into two groups based on efficacy and revealed donor-dependent variations in the anti-inflammatory effect of MSCs on macrophages as well as their immunoregulatory effect on T cells. Through surface marker analyses (242 antibodies), we found that HLA-A2 was positively related to the anti-inflammatory and immunoregulatory function of MSCs. Additionally, HLA-A2 mRNA silencing in MSCs attenuated their therapeutic effects in vitro; namely, the suppression of LPS-stimulated macrophages and phytohemagglutinin-stimulated T cells. Moreover, HLA-A2 silencing in MSCs significantly decreased their therapeutic effects in a rat model of hyperoxic lung damage. The present study provides novel insights into the quality control of donor-derived MSCs for the treatment of inflammatory conditions and diseases.
Impact of Exercise Training on Depressive Symptoms in Cancer Patients: A Critical Analysis
Authors: Marconcin Priscila , Marques Adilson , Ferrari Gerson , Gouveia Élvio R. , Peralta Miguel , Ihle Andreas
Published: April 2022

Background: Cancer patients must deal with several health challenges, including emotional distress and depressive symptoms. This study aimed to evaluate evidence from published systematic reviews and meta-analyses about the efficacy of exercise on depressive symptoms in cancer patients. Methods: We searched for previous meta-analyses of randomised controlled trials on PubMed, Web of Science and Scopus, with data inception to 30 December 2021. Two independent researchers assessed the methodological quality using the Assessment of Multiple Systematic Reviews 2 (AMSTAR2) instrument. Six meta-analyses were integrated. All included middle-aged and older adults. Five presented moderate quality, and one presented low quality. Results: Overall, a significant reduction in depressive symptoms was observed among the included studies. However, the heterogeneity between studies was high, and high-quality evidence for the efficacy of exercise on depressive symptoms was limited. Conclusions: Exercise could be a possibility in the treatment of depressive symptoms in cancer patients, especially when supervised and outside the home. The better dose of exercise needs to be clarified. More high-quality evidence is needed to better prescribe exercise to this vulnerable population.
High-Fructose Diet Increases Renal ChREBPβ Expression, Leading to Intrarenal Fat Accumulation in a Rat Model with Metabolic Syndrome
Authors: Bier Ariel , Shapira Eliyahu , Khasbab Rawan , Sharabi Yehonatan , Grossman Ehud , Leibowitz Avshalom
Published: April 2022

Fructose consumption is associated with metabolic syndrome (MeS). Dysregulated lipid metabolism and ectopic lipid accumulation, such as in “fatty liver’’, are pivotal components of the syndrome. MeS is also associated with chronic kidney disease (CKD). The aim of this study was to evaluate kidney fructose metabolism and whether the addition of fructose leads to intrarenal fat accumulation. Sprague Dawley rats were fed either normal chow (Ctrl) or a high-fructose diet (HFrD). MeS features such as blood pressure and metabolic parameters in blood were measured. The kidneys were harvested for ChREBPβ and de novo lipogenesis (DNL) gene expression, triglyceride content and histopathology staining. HK2 (human kidney) cells were treated with fructose for 48 h and gene expression for ChREBPβ and DNL were determined. The HFrD rats exhibited higher blood pressure, glucose and triglyceride levels. The kidney weight of the HFrD rats was significantly higher than Ctrl rats. The difference can be explained by the higher triglyceride content in the HFrD kidneys. Oil red staining revealed lipid droplet formation in the HFrD kidneys, which was also supported by increased adipophilin mRNA expression. For ChREBPβ and its downstream genes, scd and fasn, mRNA expression was elevated in the HFrD kidneys. Treating HK2 cells with 40 mM fructose increased the expression of ChREBPβ. This study demonstrates that fructose consumption leads to intrarenal lipid accumulation and to the formation of a “fatty kidney”. This suggests a potential mechanism that can at least partially explain CKD development in fructose-induced MeS.
Biological Control Activity of Plant Growth Promoting Rhizobacteria Burkholderia contaminans AY001 against Tomato Fusarium Wilt and Bacterial Speck Diseases
Authors: Heo A Yeong , Koo Young Mo , Choi Hyong Woo
Published: April 2022

Plant growth promoting rhizobacteria (PGPR) is not only enhancing plant growth, but also inducing resistance against a broad range of pathogens, thus providing effective strategies to substitute chemical products. In this study, Burkholderia contaminans AY001 (AY001) is isolated based on its broad-spectrum antifungal activity. AY001 not only inhibited fungal pathogen growth in dual culture and culture filtrate assays, but also showed various PGPR traits, such as nitrogen fixation, phosphate solubilization, extracellular protease production, zinc solubilization and indole-3-acetic acid (IAA) biosynthesis activities. Indeed, AY001 treatment significantly enhanced growth of tomato plants and enhanced resistance against two distinct pathogens, F. oxysporum f.sp. lycopersici and Pseudomonas syringae pv. tomato. Real-time qPCR analyses revealed that AY001 treatment induced jasmonic acid/ethylene-dependent defense-related gene expression, suggesting its Induced Systemic Resistance (ISR)-eliciting activity. Gas chromatography–mass spectrometry (GC-MS) analysis of culture filtrate of AY001 revealed production of antimicrobial compounds, including di(2-ethylhexyl) phthalate and pyrrolo [1,2-a]pyrazine-1,4-dione, hexahydro-3-(phenylmethyl). Taken together, our newly isolated AY001 showed promising PGPR and ISR activities in tomato plants, suggesting its potential use as a biofertilizer and biocontrol agent.
Cerebral Glutamate Regulation and Receptor Changes in Perioperative Neuroinflammation and Cognitive Dysfunction
Authors: Zhang Yan , Chu John-Man-Tak , Wong Gordon-Tin-Chun
Published: April 2022

Glutamate is the major excitatory neurotransmitter in the central nervous system and is intricately linked to learning and memory. Its activity depends on the expression of AMPA and NMDA receptors and excitatory amino transporters on neurons and glial cells. Glutamate transporters prevent the excess accumulation of glutamate in synapses, which can lead to aberrant synaptic signaling, excitotoxicity, or cell death. Neuroinflammation can occur acutely after surgical trauma and contributes to the development of perioperative neurocognitive disorders, which are characterized by impairment in multiple cognitive domains. In this review, we aim to examine how glutamate handling and glutamatergic function are affected by neuroinflammation and their contribution to cognitive impairment. We will first summarize the current data regarding glutamate in neurotransmission, its receptors, and their regulation and trafficking. We will then examine the impact of inflammation on glutamate handling and neurotransmission, focusing on changes in glial cells and the effect of cytokines. Finally, we will discuss these changes in the context of perioperative neuroinflammation and the implications they have for perioperative neurocognitive disorders.
Cla4p Kinase Activity Is Down-Regulated by Fus3p during Yeast Mating
Authors: Kim Junwon , Rose Mark D.
Published: April 2022

In Saccharomyces cerevisiae, the p21-activated kinase Cla4p regulates polarized morphogenesis and cytokinesis. However, it remains unknown how Cla4p kinase activity is regulated. After pheromone exposure, yeast cells temporally separate the mitotic and mating programs by sequestering Fus2p in the nucleus until cell cycle completion, after which Fus2p exits to facilitate cell fusion. Previously, we showed that sequestration is regulated by two opposing protein kinases, Cla4p and Fus3p. Phosphorylation of Fus2p-S67 by Cla4p promotes nuclear localization by both activating nuclear import and blocking export. During mating, phosphorylation of Fus2p-S85 and Fus2p-S100 by Fus3p promotes nuclear export and blocks import. Here, we find that Cla4p kinase activity is itself down-regulated during mating. Pheromone exposure causes Cla4p hyper-phosphorylation and reduced Fus2p-S67 phosphorylation, dependent on Fus3p. Multiple phosphorylation sites in Cla4p are mating- and/or Fus3p-specific. Of these, Cla4p-S186 phosphorylation reduced the kinase activity of Cla4p, in vitro. A phosphomimetic cla4-S186E mutation caused a strong reduction in Fus2p-S67 phosphorylation and nuclear localization, in vivo. More generally, a non-phosphorylatable mutation, cla4-S186A, caused failure to maintain pheromone arrest and delayed formation of the mating-specific septin morphology. Thus, as cells enter the mating pathway, Fus3p counteracts Cla4p kinase activity to allow proper mating differentiation.
Target-Specific Exosome Isolation through Aptamer-Based Microfluidics
Authors: Zhou Zixuan , Chen Yan , Qian Xiang
Published: April 2022

Exosomes (30–100 nm in diameter) are a group of cell-derived membrane vesicles, packaged as valuable cargo with lipid, proteins, and genetic materials from their parent cells. With the increasing interest in exosomes for diagnostic and therapeutic applications, the rapid isolation of pure exosome populations has become a hot topic. In this paper, we propose modified microchannels with aptamer in a microfluidics system for rapid and efficient isolation of exosomes by targeting exosome-carrying CD63 and PTK 7. The capture efficiency in surface-modified channels reaches around 107–108 particles/mL in 20 min, and purified exosomes with reliable size can be achieved.
The Efficacy of an Ultrasound-Guided Improved Puncture Path Technique of Nerve Block/Pulsed Radiofrequency for Pudendal Neuralgia: A Retrospective Study
Authors: Zhu Dan , Fan Zhenzhen , Cheng Fujun , Li Yuping , Huo Xingyue , Cui Jian
Published: April 2022

Objectives: To investigate the efficacy and safety of an improved ultrasound-guided pulsed radiofrequency (PRF) and nerve block (NB) for patients with pudendal neuralgia (PN). Methods: This retrospective analysis included 88 adults with PN treated in the Pain Department of Southwest Hospital from November 2011 to June 2021, with treatment including NB (n = 40) and PRF (n = 48). The primary outcome variable was pain severity, measured by a standardized visual analog scale (VAS). VAS values were collected at 1, 3, 7, and 14 days and 1 and 3 months after patients were treated with NB or PRF. Results: Compared with patients treated with NB (n = 40) and those treated with PRF (n = 48), no significant difference in pain reduction was observed in the short term (p = 0.739 and 0.981, at 1 and 3 days, respectively); however, in the medium and long term (1 to 3 months), there were statistically significant improvements in the PRF group over the NB group (p < 0.001). Moreover, it was noted that the average pain severity of primary PN and PN due to sacral perineurial cyst was significantly reduced with PRF therapy in the medium and long term when compared to other secondary PNs, including surgery, trauma, and diabetes. Discussion: The ultrasound-guided, improved, and innovative PRF/NB puncture path technique allows for gentler stimulation and faster identification of the pudendal nerve. The PRF technique may provide better treatments for primary PN and sacral perineurial cyst causing secondary PN in the medium and long term.
Radiosensitization of PC3 Prostate Cancer Cells by 5-Thiocyanato-2′-deoxyuridine
Authors: Zdrowowicz Magdalena , Datta Magdalena , Rychłowski Michał , Rak Janusz
Published: April 2022

Purpose: The radiosensitizing properties of uracil analogs modified in the C5 position are very interesting in the context of their effectiveness and safety in radiation therapy. Recently, radiation chemical studies have confirmed that 5-thiocyanato-2′-deoxyuridine (SCNdU) undergoes dissociation induced by an excess electron attachment and established this nucleoside as a potential radiosensitizer. In this paper, we verify the sensitizing properties of SCNdU at the cellular level and prove that it can effectively enhance ionizing radiation-induced cellular death. Methods and Materials: Prostate cancer cells were treated with SCNdU and irradiated with X rays. The cytotoxicity of SCNdU was determined by MTT test. Cell proliferation was assessed using a clonogenic assay. Cell cycle analyses, DNA damage, and cell death analyses were performed by flow cytometry. Results: SCNdU treatment significantly suppressed the proliferation and increased the radiosensitivity of prostate cancer cells. The radiosensitizing effect expressed by the dose enhancement factor is equal to 1.69. Simultaneous exposure of cells to SCNdU and radiation causes an increase in the fraction of the most radiosensitive G2/M phase, enhancement of the histone H2A.X phosphorylation level, and apoptosis induction. Finally, SCNdU turned out to be marginally cytotoxic in the absence of ionizing radiation. Conclusions: Our findings indicate that SCNdU treatment enhances the radiosensitivity of prostate cancer cells in a manner associated with the cell cycle regulation, double strand formation, and a slight induction of apoptosis.
The MST1R/RON Tyrosine Kinase in Cancer: Oncogenic Functions and Therapeutic Strategies
Authors: Cazes Alex , Childers Betzaira G. , Esparza Edgar , Lowy Andrew M.
Published: April 2022

The MST1R/RON receptor tyrosine kinase is a homologue of the more well-known MET receptor. Like MET, RON orchestrates cell signaling pathways that promote oncogenesis and enable cancer cell survival; however, it has a more unique role in the regulation of inflammation. RON was originally described as a transmembrane receptor expressed on tissue resident macrophages and various epithelial cells. RON is overexpressed in a variety of cancers and its activation modifies multiple signaling pathways with resultant changes in epithelial and immune cells which together modulate oncogenic phenotypes. While several RON isoforms have been identified with differences in structure, activation, and pathway regulation, increased RON expression and/or activation is consistently associated with worse outcomes. Tyrosine kinase inhibitors targeting RON have been developed, making RON an actionable therapeutic target.
Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information
Authors: Jing Xueping , Dorrius Monique D. , Wielema Mirjam , Sijens Paul E. , Oudkerk Matthijs , van Ooijen Peter
Published: April 2022

Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization.
The Road to Dissemination: The Concept of Oligometastases and the Barriers for Widespread Disease
Authors: AlGhamdi Hamza , Dhont Jennifer , Krayem Mohammad , De Bruyn Pauline , Engels Benedikt , Van Gestel Dirk , Van den Begin Robbe
Published: April 2022

Over the last years, the oligometastatic disease state has gained more and more interest, and randomized trials are now suggesting an added value of stereotactic radiotherapy on all macroscopic disease in oligometastatic patients; but what barriers could impede widespread disease in some patients? In this review, we first discuss the concept of oligometastatic disease and some examples of clinical evidence. We then explore the route to dissemination: the hurdles a tumoral clone has to overtake before it can produce efficient and widespread dissemination. The spectrum theory argues that the range of metastatic patterns encountered in the clinic is the consequence of gradually obtained metastatic abilities of the tumor cells. Tumor clones can obtain these capabilities by Darwinian evolution, hence early in their genetic progression tumors might produce only a limited number of metastases. We illustrate selective dissemination by discussing organ tropism, the preference of different cancer (sub)types to metastasize to certain organs. Finally we discuss biomarkers that may help to distinguish the oligometastatic state.