bluuebunny/crossref_metadata_2025 huggingface dataset:
SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network
Authors: Elwan Mohammed , Amein Ahmed S. , Mousa Aiman , Ahmed Abdelmoty M. , Bouallegue Belgacem , Eltanany Abdelhameed S.
Published: May 2022

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.
PTBP1 as a Promising Predictor of Poor Prognosis by Regulating Cell Proliferation, Immunosuppression, and Drug Sensitivity in SARC
Authors: Gong Haiyi , Jiang Aimin , Jiang Runyi , Wang Yao , Zhang Dan , Wu Zhipeng , Xiao Jianru
Published: May 2022

Background. Sarcomas (SARC) have been found as rare and heterogeneous malignancies with poor prognosis. PTBP1, belonging to the hnRNPs family, plays an essential role in some biological functions (e.g., pre-mRNA splicing, cell growth, and nervous system development). However, the role of PTBP1 in SARC remains unclear. In this study, the aim was to investigate the potential role of PTBP1 with a focus on SARC. Methods. The expression, prognostic value, possible biological pathways of PTBP1, and its relationship with immune infiltration and drug sensitivity were comprehensively analyzed based on multiple databases. PTBP1 was further validated in osteosarcoma as the most prominent bone SARC. The expression of PTBP1 was investigated through IHC. The prognostic value of PTBP1 was verified in TARGET-OS databases. CRISPR/Cas9-mediated PTBP1 knockout HOS human osteosarcoma cell lines were used to assess the effect of PTBP1 on cell proliferation, migration, metastasis and cell cycle by CCK-8, Transwell migration, invasion, and FACS experiment. Result. PTBP1 was highly expressed and significantly correlated with poor prognosis in several cancers, especially in SARC, which was validated in the clinical cohort and osteosarcoma cell lines. The genetic alteration of PTBP1 was found most frequently in SARC. Besides, PTBP1 played a role in oncogenesis and immunity through cell cycle, TGFB, autophagy, and WNT pathways at a pan-cancer level. Knockout of PTBP1 was observed to negatively affect proliferation, migration, metastasis, and cell cycle of osteosarcoma in vitro. Furthermore, PTBP1 was significantly correlated with tumor immune infiltration, DNA methylation, TMB, and MSI in a wide variety of cancers. Moreover, the potential of the expression level of PTBP1 in predicting drug sensitivity was assessed. Conclusions. PTBP1 is highly expressed and correlated with prognosis and plays a vital pathogenic role in oncogenesis and immune infiltration of various cancers, especially for SARC, which suggests that it may be a promising prognostic marker and therapeutic target in the future.
Unsplinted Attachments and Patient-Reported Outcome Measures (PROMs) in 2-Implant-Retained Mandibular Overdentures: A Systematic Review
Authors: Patil Pravinkumar G. , Seow Liang Lin , Kweh Ting Jing , Nimbalkar Smita
Published: May 2022

Introduction. The purpose of this review is to compare randomized clinical trials evaluating the patient-reported outcome measures (PROMs) using different unsplinted attachment systems in 2-implant-retained mandibular overdentures (2IRMODs). A focus question (as per PICOS) was set as follows: does one particular unsplinted attachment system (I) compared with another (C) result in better patient-reported outcomes (O) in two-implant-retained mandibular overdentures (P) using randomized controlled trials (S)? Materials and Methods. A literature search was conducted in the PubMed MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases between November 2010 and October 2020. Only randomized controlled trials (RCTs) on 2IRMOD using unsplinted attachment systems measuring patient-centered outcomes were selected. A total of 171 studies were identified in initial search, and 27 studies were shortlisted for full-text evaluation. A total of 5 studies were included for a systematic review. The risk of bias was evaluated using Cochrane Risk of Bias Tool 2.0 (RoB 2.0). Meta-analysis could not be performed as different studies evaluated different patient-reported outcomes, namely, satisfaction, quality of life, complications, preferences, or combinations of these. Results. A total of 23 patients received low-profile (self-aligning) attachments (in 2 studies), 69 patients received standard ball attachments (in 5 studies), 25 patients received telescopic (or conus) attachments (in 2 studies), and 20 patients received mini-ball attachments (in 1 study). Two studies compared ball attachments and low-profile attachments and revealed similar satisfaction and quality of life (QoL). Two studies compared ball attachments with telescopic attachments and revealed less patient satisfaction in telescopic attachments. A single study compared mini-ball attachments with standard ball attachments and showed no difference in patient-reported outcomes. Three studies were found to have a low risk of bias, and the remaining two studies had a high risk of bias. Conclusions. The standard ball, mini-ball, and low-profile attachments have no influence on PROMs in the normal interarch space. Inconclusive results were found in studies that evaluated PROMs using ball attachments versus telescopic attachments.
Effect of Collagen/Ibuprofen Hydrogel in Wound Healing: An In Vivo Study
Authors: Abbasi Kamyar , Eftekhar Ashtiani Reza , Abdolahi Mitra , Hosseini Maryam , Sayyad Soufdoost Reza , Alam Mostafa , Fani-Hanifeh Sadaf
Published: May 2022

Background and Aim. Wound healing is a complicated physiological process to preserve skin integrity after injuries and includes the proliferative phase, hemostasis/inflammatory phase, and remodeling through extracellular, intercellular, and intracellular components synchronization. The study aimed to examine human placental collagen/ibuprofen’s effect on wound healing in an animal model. Materials and Methods. The cell viability test was performed by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay. In this animal study, three circular excisions were made in the back of each of the 12 rats and injected with the following groups: collagen/ibuprofen, group 1; collagen, group 2; and unfilled as the control group, group 3 for two times. The healing procedures were explored via calculating wound contraction percentage after taking photographs on days 7 and 14. Also, histopathological assessments were conducted on days 7 and 14. Results. The MTT results showed no significant cytotoxicity, and macroscopic results demonstrated that the percentage of wound contraction was 85%, 79%, and 41% in the collagen/ibuprofen, collagen, and control groups on 14th day, respectively. Histopathological assessments showed that hair follicles number, fibroblasts content, and angiogenesis in the collagen/ibuprofen were remarkably more significant than collagen and control groups. Conclusion. The collagen/ibuprofen group had significant outcomes compared with collagen and the control groups in wound healing and wound contraction. Collagen/ibuprofen hydrogel can be a potential hydrogel in accelerating wound healing.
Preventing Axonal Sodium Overload or Mitochondrial Calcium Uptake Protects Axonal Mitochondria from Oxidative Stress-Induced Alterations
Authors: Ulshöfer Rebecca , Bros Helena , Hauser Anja Erika , Niesner Raluca Aura , Paul Friedemann , Malla Bimala , Infante-Duarte Carmen
Published: May 2022

In neuroinflammatory and neurodegenerative disorders such as multiple sclerosis, mitochondrial damage caused by oxidative stress is believed to contribute to neuroaxonal damage. Previously, we demonstrated that exposure to hydrogen peroxide (H2O2) alters mitochondrial morphology and motility in myelinated axons and that these changes initiate at the nodes of Ranvier, where numerous sodium channels are located. Therefore, we suggested that mitochondrial damage may lead to ATP deficit, thereby affecting the efficiency of the sodium-potassium ATPase and eventually leading to sodium overload in axons. The increased intra-axonal sodium may revert the axonal sodium-calcium exchangers and thus may lead to a pathological calcium overload in the axoplasm and mitochondria. Here, we used the explanted murine ventral spinal roots to investigate whether modulation of sodium or calcium influx may prevent mitochondrial alterations in myelinated axons during exogenous application of H2O2 inducing oxidative stress. For that, tetrodotoxin, an inhibitor of voltage-gated sodium ion channels, and ruthenium 360, an inhibitor of the mitochondrial calcium uniporter, were applied simultaneously with hydrogen peroxide to axons. Mitochondrial shape and motility were analyzed. We showed that inhibition of axonal sodium influx prevented oxidative stress-induced morphological changes (i.e., increase in circularity and area and decrease in length) and preserved mitochondrial membrane potential, which is crucial for ATP production. Blocking mitochondrial calcium uptake prevented decrease in mitochondrial motility and also preserved membrane potential. Our findings indicate that alterations of both mitochondrial morphology and motility in the contexts of oxidative stress can be counterbalanced by modulating intramitochondrial ion concentrations pharmacologically. Moreover, motile mitochondria show preserved membrane potentials, pointing to a close association between mitochondrial motility and functionality.
Martingale Decomposition and Backward Stochastic Dynamic Equations on Time Scales
Authors: Tang Guofeng , Jia Guangyan
Published: May 2022

The paper aims to establish the related backward stochastic dynamic equations on time scales, BS ∇ Es for short, concerning to ∇‐integral on time scales. We present the martingale decomposition theorem on time scales and prove the existence and uniqueness theorem of solutions to BS ∇ Es. This work can be considered as a unification and a generalization of similar results in backward stochastic difference equations and backward stochastic differential equations.
Integrated UPLC-Q-TOF-MS/MS and Network Pharmacology Approach to Investigating the Metabolic Profile of Marein of Coreopsis tinctoria Nutt.
Authors: Liu Jing , Cheng Xuejing , Zheng Xin , Shi Yumeng , Li Chunxia , He Qiaoyu , Li Yue , Chen Xiaopeng
Published: May 2022

Marein is the main active compound of Coreopsis tinctoria Nutt., and its main activities include antioxidant, hypoglycemic, and hypotensive. After oral administration of marein, the blood concentration of marein is low. The metabolites of marein have not been reported systematically. In this study, a rapid and systematic method based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) was established to detect metabolites of marein in vivo (plasma and urine) after oral administration and injection. Sixty-one metabolites were identified. The metabolites are formed through a wide range of metabolic reactions, including hydroxylation, glucuronidation, methylation, hydrolysis, and desorption of hydrogen. The liver microsome incubation was further used to investigate the metabolic rate of marein. Network pharmacology was applied to study the targets and pathways of marein and its metabolites. Marein and its metabolites act on the same targets to enhance the therapeutic effect. This research illuminates the metabolites and metabolic reaction of marein and establishes a basis for the development and rational utilization of C. tinctoria. Meanwhile, the analysis of prototype and metabolites together by network pharmacology techniques could provide a methodology for the study of component activity.
Intelligent Malfunction Identification Method in Mechanical Manufacturing Process Based on Multisensor Data
Authors: Wang Meng
Published: May 2022

Current technology trends have been gradually integrated into the production of all walks of life, which play an indispensable part in promoting the intelligent development of enterprises, and have brought a greater impact on production and reformation. With the rapid development of the economy and technology, the manufacturing industry has played a very important role. For this reason, the introduction of artificial intelligence into machinery manufacturing can not only improve production efficiency but also save labor and reduce labor costs. The application of artificial intelligence in machinery manufacturing has a critical good role in promoting industrial upgrading and transformation. This time, through the application of smart algorithms in machinery manufacturing and its automation, we expect that such a technological revolution can provide a new development prospect for the development of manufacturing intelligence and automation. Taking the malfunction identification of string striking machinery as an example, this paper studies the smart identification method of mechanical malfunction based on multisensor. In the process of malfunction identification of keyboard stroke machinery, the accuracy of malfunction identification results is low due to the influence of the identification model. Moreover, a malfunction identification and analysis method for keyboard stroke machinery based on BP optimized by GA is proposed. The mechanical data of keyboard chords are acquired by sound‐sensitive sensors, and the data features are extracted by wavelet packet decomposition. Based on the optimized BP, a mechanical malfunction judgment model is constructed, and various parameters in the model are calculated. The results show that the intelligent identification method proposed has exhibited strong adaptability and superiority compared with the traditional method.
UPLC‐ESI‐TOF‐MS Analysis of the Effect of Dendrimers Core Lengths on Their Molecular Profiles and Purity
Authors: Abdelhady Hosam Gharib , Aleanizy Fadilah Sfouq , Alqahtani Fulwah Yahya , Alkahtani Hamad
Published: May 2022

The effect of core length of Polyamidoamine (PAMAM) dendrimers on their physicochemical properties and hence their purity was studied by ultraperformance liquid chromatography‐electrospray ionization‐time of flight‐mass spectrometry (UPLC‐ESI‐TOF‐MS). Seven consecutive generations Gn = (G0-6) of both Ethylenediamine core (C2) and Diaminobutane core (C4) dendrimers were tested. The separation and detection of each generation of dendrimers (covering a molar mass range of 517‐58,076 Da and a radius range of 15‐67 Å) were performed within 10 min. Increasing the length of the core by the C2H4 group significantly changed the morphological characteristics of the dendrimers. For example, the general morphology of C4 dendrimers becomes less compact than C2 dendrimers. This facilitates the influx of impurities from the inner nanocavities of these molecules, even at higher generations, during the UPLC run providing C4 dendrimers of higher purity than C2 dendrimers. These results reveal that the toxicity found by some researchers due to the application of dendrimers may be mostly due to leakage of the encapsulated starting materials, which can be eliminated by using optimized dendritic molecules.12
Effects of Groundwater Depth and Salt Content on Vegetation in Dry Lake Basins: A Case Study of Chahan Lake, Northern China
Authors: Chen Peng , Ma Rong , Shi Jiansheng , Si Letian
Published: May 2022

Under the dual influences of global climate change and human activities, inland lakes in arid areas are shrinking and drying up, and a large area of bare lake bed has become the source of the release of chemical dust. The aim of this study is to study the control of groundwater on the distribution and development of natural vegetation and the effects of the groundwater conditions on soil salinization. In this study, a typical modern dry lake in northern China, Chahan Lake, was taken as the study area. Through field investigations, field sampling and analysis, and statistical analysis, the influence of groundwater on the ecosystem of this dry lake was studied. The results revealed that the vegetation communities in the lakeside zone were Kalidium foliatum, Nitraria tangutorum, Suaeda glauca, Leymus chinensis, Chloris virgata, and Carex duriuscula communities from the dry lake bed outwards. The groundwater table suitable for vegetation growth in Chahan Lake is 2.0–3.0 m deep. The groundwater table suitable for the growth of Kalidium foliatum vegetation is 1.5–2.5 m deep. The groundwater table suitable for the growth of Leymus chinensis vegetation is 3.0–4.0 m deep. In Chahan Lake, the critical groundwater depth and total dissolved solids (TDS) for moderate salinization, severe salinization, and saline soil occurrence are 4.0 m and 2.0 g/L, 3.0 m and 3.0 g/L, and 1.5 m and 4.0 g/L, respectively. Regarding the prevention and control of salt-dust storms, the ecological threshold of the groundwater, which can effectively increase the vegetation coverage and prevent soil salinization, is groundwater depths of 2.0–4.0 m and TDS values of <2 g/L.
Multiobjective Optimization Strategy of WSN Coverage Based on IPSO-IRCD
Authors: Wu Juan , Li Hongbing , Luo Lei , Ou Jun , Zhang Ying
Published: May 2022

The nonuniform distribution characteristic of randomly deployed mobile nodes will lead to the coverage hole and redundancy in wireless sensor networks (WSNs). To solve this problem, we propose a multiobjective optimization algorithm for WSN based on Improved Particle Swarm Optimization-Increment of the Ratio of Coverage Rate to Move Distance (IPSO-IRCD), and a network node coverage optimization model is formulated to maximize the coverage rate of the target area while reducing the moving distance of nodes. In each iteration of IPSO, the population fitness value is calculated and compared with the historical optimal value, when the arbitrary dimensional location information of each node is updated, which can avoid the standard PSO algorithm loses the optimal solution, and IPSO will determine the candidate deployment location of nodes. Based on which, IRCD node coverage scheduling optimization algorithm is proposed, so that the final deployment location can be determined iteratively by calculating IRCD of nodes. Simulation results indicate that, for the nodes initial coverage state follows random distribution and Gaussian distribution, IPSO-IRCD can, respectively, improve 4.6% and 7.4% coverage ratio compared with the suboptimal algorithm in other five similar algorithms and reduce 809.59 m and 626.63 m nodes moving distance.
[Retracted] Evaluation of MWCNT Particles‐Reinforced Magnesium Composite for Mechanical and Catalytic Applications
Authors: Sathish T. , Mohanavel Vinayagam , Velmurugan Palanivel , Alfarraj Saleh , Al Obaid Sami , Sureshkumar Shanmugam , Joshua Ramesh Lalvani J. Isaac
Published: May 2022

Aluminum, magnesium, and copper materials must have increased mechanical strength with enhanced wear and corrosion resistance. Substantial research focused on reinforcing hard particles into low‐strength materials using stir casting or powder metallurgy. This work is intended to develop the magnesium hybrid matrix with the dispersion of boron carbide (B4C) and multiwall carbon nanotubes (MWCNTs). Hybrid magnesium composites are prepared, although the powder metallurgy route considers different process parameters. Statistical analysis such as Taguchi L16 orthogonal array is involved in this work. It is used to find the magnesium hybrid samples’ minimum and maximum wear, corrosion, and microhardness levels. Powder metallurgy parameters are B4C (3%, 6%, 9%, and 12%), MWCNT (0.2%, 0.4%, 0.6%, and 0.8%), ball milling (1, 2, 3, and 4 h), and sintering (3, 4, 5, and 6 h). The ball milling parameters are extremely influenced in the wear test analysis. Minimum wear losses are obtained as 0.008 g by influencing the 4 h ball milling process. Similarly, 3 h of sintering time offered a minimum corrosion rate of 0.00078 mm/yr. In microhardness analysis, the percentage of MWCNTs is highly implicated in narrow hardness resulting in the hardness value of 181. The hardness value is recorded using 0.2% MWCNTs in the magnesium alloy AZ80.
Low-Dose Gallic Acid Administration Does Not Improve Diet-Induced Metabolic Disorders and Atherosclerosis in Apoe Knockout Mice
Authors: Bai Jie , Lin Qiu-Yue , An Xiangbo , Liu Shuang , Wang Yao , Xie Yunpeng , Liao Jiawei
Published: May 2022

Diets rich in polyphenols are known to be beneficial for cardiovascular health. Gallic acid (GA) is a plant-derived triphenolic chemical with multiple cardio-protective properties, such as antiobesity, anti-inflammation, and antioxidation. However, whether GA could protect against atherosclerotic cardiovascular diseases is still not defined. Here, we investigated the effects of low-dose GA administration on diet-induced metabolic disorders and atherosclerosis in the atherosclerosis-prone apolipoprotein E (Apoe) knockout mice fed on a high-fat Western-type diet (WTD) for 8 weeks. Our data showed that GA administration by oral gavage at a daily dosage of 20 mg/kg body weight did not significantly ameliorate WTD-induced hyperlipidemia, hepatosteatosis, adipogenesis, or insulin resistance; furthermore, GA administration did not significantly ameliorate WTD-induced atherosclerosis. In conclusion, our data demonstrate that low-dose GA administration does not elicit significant health effect on diet-induced metabolic disorders or atherosclerosis in the Apoe knockout mice. Whether GA could be beneficial for atherosclerotic cardiovascular diseases therefore needs further exploration.
Complex Management Countermeasures of Postgraduate Education Quality Based on Comparison of International Training Models
Authors: Wan Youling , Guo Zhiming
Published: May 2022

Graduate students are an important engine of national and regional development and the dominant force of contemporary scientific and technological innovation. It has become the historical mission of high‐level research universities to provide strong talent support for the construction of innovative powerful countries with human resources. This investigation performed a systematic and comparative analysis of international postgraduate training models. The whole process of multidimensional training countermeasures and suggestions were proposed. Those specifically included updating the training concept, optimizing the curriculum structure, innovating teaching methods, strengthening practical training, strengthening the responsibility of the tutor, and expanding the international vision. After investigation and analysis, this research pointed out that the conditions of establishing and perfecting the examination system, evaluation mechanism, and incentive measures related to postgraduate education were guaranteed. Multiple measures to improve the quality of graduate students have certain reference significance for the current graduate education.
Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning
Authors: Zhao Ziliang , Zhu Qinglin , Guo Bin
Published: May 2022

This study develops an adaptive dynamic programming (ADP) scheme for uncertain systems to achieve the robust trajectory tracking. In this framework, the augmented state is first established via combining the tracking error and reference trajectory, where the robust tracking control problem can be resolved using the regulation control strategy. Then, the robust control problem of uncertain system can be represented as an optimal control problem of nominal system, which provides a new pathway to address the robust control problem. To realize the optimal control, the derived Hamilton–Jacobi–Bellman equation (HJBE) is solved by training a critic neural network (CNN). Finally, two innovative critic learning techniques are suggested to calculate the unknown NN weights, where the convergence of NN weights can be guaranteed. Simulations are carried out to demonstrate the effectiveness of the proposed method.
Identification of Significant Secreted or Membrane-Located Proteins in Laryngeal Squamous Cell Carcinoma
Authors: Yan Li , Hu Chunyan , Ji Yangyang , Zou Lifen , Zhao Yang , Zhu Yi , Wang Xiaoshen
Published: May 2022

Background. This study is aimed at investigating the expressions and prognostic values of secreted or membrane-located proteins (SMPs) in laryngeal squamous cell carcinoma (LSCC). The correlations between the expressions of SMPs and immune cells’ infiltrations were also investigated. Methods. The expression data of normal laryngeal and LSCC samples were obtained from the TCGA and GEO datasets. The differentially expressed SMPs were identified, and their prognostic values were analyzed. The biological functions of differentially expressed and worse-survival-related SMPs were explored. LASSO regression, Cox multivariate analysis, and nomogram were used to construct a model to predict the survival. Then, the infiltrations of the 24 immune cell populations were calculated using the GSVA method, and the correlations between the expression of SMPs and the immune infiltration were investigated. Results. 122 samples (12 normal and 120 LSCC) of the TCGA database and 114 samples (57 normal and 57 LSCC) of GSE127165 were included. We identified that 138 SMPs were significantly upregulated in LSCC samples of both the TCGA and GEO datasets, among which 52 SMPs were significantly correlated with worse survival. GO and KEGG analyses revealed those 52 SMPs significantly participate in tumor microenvironment and immune cells’ communication. Nine of 52 SMPs (ABCC5, ATP1B3, CLEC11A, FLNA, FSTL3, MMP1, NME1, OAS3, and PHLDB2) were included in the nomogram to effectively and accurately predict the LSCC patients’ survival. The expressions of most SMPs, such as MMP1 and FSTL3, were significantly positively correlated with the immune infiltration of LSCC. Conclusions. In this study, the expression, prognostic values, and correlations with immune infiltration of SMPs were analyzed in LSCC samples. Our analyses identified several significant SMPs differentially expressed between normal laryngeal and LSCC samples, correlated with worse survival, and related to the immune infiltration.
Plasma Levels of Bioactive Vitamin D and A5 Ligands Positively Correlate with Clinical Atopic Dermatitis Markers
Authors: Lucas Renata , Szklenar Monika , Mihály Johanna , Szegedi Andrea , Töröcsik Daniel , Rühl Ralph
Published: May 2022

Background: Over the past decade, several controversial studies described a relationship between vitamin D and atopic diseases. Low plasma vitamin D levels or even vitamin D deficiency was associated with an increased incidence of atopic disease, postulating that a higher dietary intake of vitamin D may be a beneficial strategy against atopic diseases such as atopic dermatitis (AD). Objective: Our aim was to determine the relationship between plasma 25-hydroxyvitamin D3 (25(OH)D3) levels, the levels of the ligand of the vitamin D receptor (VDR) heterodimerization partner as well as the retinoid X receptor (RXR) and the active vitamin A5 derivative 9-cis-13,14-dihydroretinoic acid (9CDHRA) and AD severity. Methods/Results: Samples from AD patients (n = 20) and healthy volunteers (n = 20) were assessed. In our study, the frequently measured VDR ligand precursor 25(OH)D3 in addition to the VDR-ligand 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) and 9CDHRA displayed no different levels when compared with the plasma of AD patients and healthy volunteers. When performing further correlation studies focusing on AD patients, plasma 25(OH)D3 levels showed a negative correlation with eosinophils in blood (EOS) and SCORing Atopic Dermatitis (SCORAD) values, while 1,25(OH)2D3 and 9CDHRA levels correlated positively with plasma IgE, EOS, and SCORAD values. Conclusion: In consequence, the metabolic activation of vitamin D from 25(OH)D3 towards 1,25(OH)2D3 as well as the co-liganding of the RXR by 9CDHRA may be an important signalling mechanism, an important marker for AD development and severity as well as the basis for novel nutritional and pharmaceutical AD treatment options.
Pseudodicentric Chromosome Originating from an X-Autosome Translocation in a Male Patient with Cryptozoospermia
Authors: Toujani Saloua , Tucker Elena J. , Akloul Linda , Mary Laura , Pimentel Céline , Launay Erika , Freton Lucas , Jouve Guilhem , Henry Catherine , Odent Sylvie , Belaud-Rotureau Marc-Antoine , Jaillard Sylvie
Published: May 2022

Genetic factors are responsible for 15% of male infertility conditions. Numerical and structural chromosomal anomalies are validated genetic factors leading to spermatogenic quantitative defects, with a frequency depending on the severity of the phenotype. Among the structural chromosomal rearrangements, dicentric chromosomes are generally observed in robertsonian translocations or in cases of Y chromosome isodicentrics. In X-autosome translocations, male carriers are generally infertile, regardless of the position of the breakpoint, due to interrupted spermatogenesis. We report an infertile man bearing an unusual balanced (X;22) translocation, with a centromeric X breakpoint generating a derivative pseudodicentric chromosome psu dic(22;X). Extensive cytogenetic analyses were necessary to determine the precise nature of the derivative chromosome. The likely cause of the reproductive phenotype of the patient is discussed based on meiotic chromosomal conformation.
Effects of App-Based Mobile Interventions for Dementia Family Caregivers: A Systematic Review and Meta-Analysis
Authors: Shin YunHee , Kim Sun Kyung , Kim Yumi , Go Younghye
Published: May 2022

Background: Dementia was caregivers experience tremendous difficulties both physically and psychologically, leading to high levels of depression and caregiver burden. The advantage of mobile interventions has been recognized due to its freedom from time and space restrictions. Objectives: A systematic review and meta-analysis was conducted to evaluate effectiveness of app-based mobile interventions for dementia caregivers. Method: We searched nine different databases including CINAHL, the Cochrane Library, Embase, MEDLINE, PsycINFO, the ACM digital library, IEEE Xplore, KoreaMed, and RISS for publications on app-based mobile interventions targeting dementia caregivers published in English or Korean. Meta-analysis was conducted using Comprehensive Meta-Analysis (CMA) version 3.0. Standard mean difference (SMD) was used to estimate the effectiveness of the intervention on caregiver-related outcomes of caregiver burden, depression, stress, caregiving competency, and quality of life (QoL). Results: Five studies (three randomized controlled trials and two quasi-experimental studies) with a total of 230 participants were included. Using a mobile device, interventions were used to provide information and feedback, perform monitoring, and conduct skill training. Pooled analysis showed favorable effects of app-based mobile interventions for caregivers on caregiver burden (SMD = −0.315, 95% CI: −0.681 to 0.052), depression (SMD = −0.236, 95% CI: −0.517 to 0.046), stress (SMD = −0.295, 95% CI: −0.708 to 0.118), competency (SMD = 0.434, 95% CI: 0.093–0.775), and QoL (SMD = 0.794, 95% CI: 0.310–1.278). Conclusion: Types of mobile devices, lengths, and contents of interventions varied between included studies. Given that only five studies were included, the current meta-analysis could not confirm the effectiveness of app-based mobile interventions. However, this study suggests that app-based mobile interventions for dementia caregivers might have positive effects on diverse caregiving-related issues such as caregiver burden, depression, stress, competency, and QoL.
History of the Scientific School of Z. A. Mansurov at the Institute of Combustion Problems in Almaty
Authors:   Al-Farabi Kazakh National University, Usenov Galymzhan , Suleimenov Pirimbek ,   Al-Farabi Kazakh National University, Müürsepp Peeter ,   Tallinn University of Technology
Published: May 2022

The article discusses the origins, formation, and development of the scientific school of chemical physics and nanotechnology in Kazakhstan. The authors describe the achievements of a scientific school that is on par with the best of its kind locally and globally, adding new competitive results of practical relevance to the economic development of the country. The article also highlights the special role of the outstanding scientist Zulkhair Aimukhametovich Mansurov in the development of the methodological foundations of the scientific school, the targeted training of the scientific personnel in the priority areas of the development of Kazakhstani chemical physics, and the creation of nanoscale materials for multifunctional purposes. Also, the main principles of the philosophy of Mansurov’s scientific school are presented.
Design of field programmable gate array-based data processing system for multi global positioning system receiver
Authors: Abidin Zainul , Aryawiratama Nauval , Muttaqin Adharul , Miyauchi Ryoichi
Published: May 2022

A global positioning system (GPS) sensor is needed for a ballistic/moving object to do position tracking. In previous study, a multi GPS processing system was made using several microcontrollers and data processing cannot be done simultaneously. Therefore, it was considered as ineffective system. In this research, field programmable gate array (FPGA)-based data processing system for multi-GPS receiver was proposed. The proposed system was designed to reduce root mean square error (RMSE). There are two main processes in the proposed system which work in parallel, i.e. data parsing and data processing. Raw data from GPS receiver was collected and calculated to get average value, then sent it through serial communication to show result. Experimental results confirm the RMSE value of the proposed system is smaller than the conventional one. The RMSE for latitude, longitude, and altitude decrease by 38.46%, 58.28%, and 24.80%, respectively.
Acute lymphoblastic leukemia detection approach from peripheral blood smear using color threshold and morphological techniques
Authors: Al Mamun Abdullah , Hossen Md. Jakir , Tahabilder Anik , Musha Ahmmad , Hasnat Rehnuma , Saha Sohag Kumar
Published: May 2022

Acute lymphoblastic leukemia (ALL) has recently been one of the most significant concerns in cancers, especially child and old age. Therefore, crying needs to diagnose leukemia as early as possible, increasing the treatment options and patient survivability. Some basic handicraft leukemia detection processes have been introduced in this arena though these are not so accurate and efficient. The proposed approach has been introduced an automated ALL recognition system from the peripheral blood smear. Initially, the color threshold has been applied to segment lymphocytes blood cells from the blood smear. Some post-processing techniques like morphological operation and watershed have been executed to segment the particular lymphocytes cell. Finally, we used a support vector machine (SVM) classifier to classify the cancerous image frames using a statistical feature vector obtained from the segmented image. The proposed framework has achieved the highest accuracy of 99.21%, the sensitivity of 98.45%, specificity of 99%, the precision of 99%, and F1 score of 99.1%, which has beat existing and common states of art methods. We are confident that the proposed approach will positively impact the ALL detection arena.
A typical analysis of hybrid covert channel using constructive entropy analytics
Authors: Krishnamurthy Koundinya Anjan , Hebbur Satyanarayana Gururaja
Published: May 2022

A covert timing channel is based on modulation of the timing information in the network packets in a secured communication. The delicacy of this channel is primarily viewed as single coherent channel thwart the detection from any third-party entity or network admin. The timing covert channel is strenuous to detect under many scenarios due to the intricate complexity of the channel. The exploration of timing covert channel shed light on intrinsic design aspects which elevate understanding to an advanced level. This will effectively bring out elite literature aspects of the timing covert channel for seamless implementation. Supraliminal channels are innocuous message-based channel introduced as a trapdoor in the communication system either intentional or as vulnerability of the system. A hybrid covert channel is the existence of homogeneous or heterogeneous network covert channel variants either at same instant or at different instant of time. For instance, one of possible hybrid covert channel is the co-existence of timing covert channel in transmission control protocol (TCP) and supraliminal channel in voice over internet protocol (VoIP). This paper introduces this variant of the hybrid covert channel and their significance in network communication. The paper also refers to standard measures-entropy, covertness index to understand hybrid covert channel.
Evolution of wireless communication networks: from 1G to 6G and future perspective
Authors: Ahmed Solyman Ahmed Amin , Yahya Khalid
Published: May 2022

Since about 1980, a new generation has appeared approximately every decade. Mobile phones started with first-generation (1G), then the successful second generation (2G), and then mixed successful auctions since the launch of 3G. According to business terms, 1G and 2G were providing voice and gradually include data (3G is unsuccessful, 4G is very successful). Today, we are seeing a stir over what 5G will provide. Key expectations currently being discussed include an ultra-high 20 Gb/s bit rate, an ultra-low latency of just 1 millisecond, and a very high capacity. Given the enormous potential of 5G communication networks and their expected evolution, what should 6G include that is not part of 5G or its long-term evolution? 6G communication networks should deliver improved range and data speeds, as well as the ability to connect users from anywhere. This article details possible 6G communication networks. More specifically, the primary influence of this research is to deliver a complete synopsis of the development of wireless communication networks from 1G to 6G.
Open distance learning simulation-based virtual laboratory experiences during COVID-19 pandemic
Authors: Sazanita Isa Iza , Abdullah Hasnain , Kasim Nazirah Mohamat , Ismail Noor Azila , Faiza Zafirah
Published: May 2022

The widespread of coronavirus disease 2019 (COVID-19) pandemic led to a discovery that open distance learning (ODL) has turned out to be the only choice for teaching and learning by most institution (s) of higher learning (IHLs). In Malaysia, ODL is considered a new approach as physical laboratory practice has always been conducted for laboratory courses. This is a quantitative study which explores the perceptions of e-Lab among the students of bachelor’s in electrical and electronic engineering (EE) by focusing on the effectiveness and readiness in conducting the e-Lab. Simulation-based model is proposed for conducting the e-Lab using an interactive media and validated with the final score performance. With the future goals of improving the e-Lab in terms of delivering methods and engaging mediums between students and laboratory instructor, this study also discovered the levels of response from students’ perception to substitute the conventional laboratory by providing an equivalent and comparable learning experiences of the students.
Analyzing sentiment dynamics from sparse text coronavirus disease-19 vaccination using natural language processing model
Authors: Govindappa Jalaja , Channegowda Kavitha
Published: May 2022

Social media platforms enable people exchange their thoughts, reactions, emotions regarding all aspects of their lives. Therefore, sentiment analysis using textual data is widely practiced field. Due to large textual content available on social media, sentiment analysis is usually considered a text classification task. The high feature dimension is an important issue that needs to be resolved by examining text meaningfully. The proposed study considers a case study of coronavirus (COVID) vaccination to conclude public opinions about prospects for vaccination. Text corpus of tweets is collected, published between December 12, 2020, and July 13, 2021 is considered. The proposed model is developed considering phase-by-phase data analysis process, followed by an assessment of important information about the collected tweets on coronavirus disease (COVID-19) vaccine using two sentiment analyzer methods and probabilistic models for validation and knowledge analysis. The result indicated that public sentiment is more positive than negative. The study also presented statistics of trends in vaccination progress in the top countries from early 2021 to July 2021. The scope of study is enormous regarding sentiment analysis based on keyword and document modeling. The proposed work offers an effective mechanism for a decision-making system to understand public opinion and accordingly assists policymakers in health measures and vaccination campaigns.
NAGA: multi-blockchain based decentralized platform architecture for cryptocurrency payment
Authors: Sawarnkatat Dendej , Smanchat Sucha
Published: May 2022

Paying for electronic commerce products with cryptocurrencies is an increasingly popular method. However, the situations where a seller expects one specific cryptocurrency as a payment while a buyer only possesses another, inevitably create inconvenience to the buyer, which may lead to cancellation of purchase. In this paper, we propose a light-weighted software architecture of a payment system called NAGA platform that works with a number of crypto blockchain networks to support cross-cryptocurrency payments where buyers can pay in one currency, and the seller automatically receives another of his/her choice. This minimizes the complexity and inconvenience of the buyer, leading to an increase in sales and revenues of the electronic commerce system. With the built-in crypto exchange, crosscryptocurrency payment can be processed with real-time exchange rates that enables both buyers and sellers to receive cryptocurrency of their preferred choices.
Directional movement index based machine learning strategy for predicting stock trading signals
Authors: Saud Arjun Singh , Shakya Subarna
Published: May 2022

Intelligent stock trading systems are demand of the modern information age. This research paper proposed a directional movement index based machine learning (DMI-ML) strategy for predicting stock trading signals. Performance of the proposed strategy was evaluated in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of profitable trades executed by the trading strategy. In addition, performance of the proposed model was evaluated against the strategies viz. traditional DMI, Buy-Hold. From the experimental results, we observed that the proposed strategy outperformed other strategies in terms of all three parameters. On average, the ARR obtained from the DMI-ML strategy was 52.58% higher than the ARR obtained from the Buy-Hold strategy. At the same time, the ARR of the proposed one was found 75.12% higher than the ARR obtained from the traditional DMI strategy. Furthermore, the Sharpe ratio for the DMI-ML strategy was positive for all stocks. On the other side, the percentage of profitable trades executed by the DMI-ML strategy soared in comparison to the percentage of such trades by the traditional DMI. This study also extended analysis of the proposed model with the various intelligent trading strategies proposed by authors in various literatures and concluded that the proposed DMI-ML strategy is the better strategy for stock trading.
Efficient lane marking detection using deep learning technique with differential and cross-entropy loss
Authors: Al Mamun Abdullah , Em Poh Ping , Hossen Md. Jakir , Tahabilder Anik , Jahan Busrat
Published: May 2022

Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learning-based model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.
Scalable decision tree based on fuzzy partitioning and an incremental approach
Authors: Lotfi Somayeh , Ghasemzadeh Mohammad , Mohsenzadeh Mehran , Mirzarezaee Mitra
Published: May 2022

Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then Fuzzy Information Gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.