bluuebunny/crossref_metadata_2025 huggingface dataset:
Phenolic allelochemicals: Achievements, limitations, and prospective approaches in weed management
Authors: Hoang Anh La , Van Quan Nguyen , Tuan Nghia La , Dang Xuan Tran
Published: June 2021

In recent decades, phenolic compounds derived from plants have been considered as potential weed inhibitors due to their environmentally friendly properties, which are profitable regarding sustainable development targets. While experiments indicated that phenolic compounds exhibited significant inhibition on weeds via various biological pathways, none of these natural compounds have been efficiently applied in practical agricultural production. This paper provides an overview of the phenolic allelochemicals (and their plant sources) that have been reported to have the most potential for weed growth inhibition as well as their modes of action. The biosynthetic pathway, classification, distribution, release, degradation, extraction, and isolation of these allelochemicals are also discussed. In addition, the advantages and limitations of applying these allelochemicals in weed management and sustainable agricultural development are indicated. Accordingly, there are three promising strategies to deal with the present problems and improve the effectiveness of future studies, including: (a) to discover a new approach combining phenolic allelochemicals and other prospective phytochemicals in the development of natural herbicides; (b) to modify the chemical structure for strengthening the stability and activity of the target compound; and (c) to generate resistant crops against weed stress related to the proliferation of phenolic allelochemicals through genetic engineering approaches. Conceivably, the information on phenolic allelochemicals that is assembled in this paper may provide a useful dataset for future studies on the application of artificial intelligence in the design and production of efficient natural herbicides for weed management and sustainable agricultural development.
Across-arc variations in Mo isotopes and implications for subducted oceanic crust in the source of back-arc basin volcanic rocks
Authors: Li Xiaohui , Yan Quanshu , Zeng Zhigang , Fan Jingjing , Li Sanzhong , Li Jie , Yang Huixin , Wang Xiaoyuan
Published: June 2021

Molybdenum (Mo) isotope ratios provide a potential means of tracing material recycling involved in subduction zone processes. However, the geochemical behavior of Mo in subducted oceanic crust remains enigmatic. We analyzed Mo isotope ratios of arc and back-arc basin lavas from the Mariana subduction zone (western Pacific Ocean), combining newly obtained element and Sr-Nd-Pb-Li isotope data to investigate subduction zone geochemical processes involving Mo. The Mo isotope ratios (δ98/95MoNIST3134; U.S. National Institute of Standards and Technology [NIST] Mo standard) of the volcanic rocks showed clear across-arc variations, decreasing with increasing depth to the Wadati-Benioff zone. The high δ98/95Mo values in the Mariana Islands (−0.18‰ to +0.38‰) correspond to high 87Sr/86Sr, low 143Nd/144Nd, and radiogenic Pb isotope ratios, suggesting that altered upper oceanic crust played an important role in the magma source. The low δ98/95Mo values in the Central Mariana Trough (−0.65‰ to −0.17‰) with mantle-like Sr-Nd-Pb but slightly low δ7Li values provide direct evidence for the contribution of deep recycled oceanic crust to the magma source of the back-arc basin lavas. The isotopically light Mo magmas originated by partial melting of a residual subducted slab (eclogite) after high degrees of dehydration and then penetrated into the back-arc mantle. This interpretation provides a new perspective with which to investigate the deep recycling of subducted oceanic lithosphere and associated magma petrogenesis.
Supplemental Material: Seismic fault weakening via CO2 pressurization enhanced by mechanical deformation of dolomite fault gouges
Authors: Kim Hyun Na , So Byung-Dal , al. et
Published: June 2021

Experimental and numerical methods.
Supplemental Material: Construction of the Lesser Himalayan–Subhimalayan thrust belt: The primary driver of thickening, exhumation, and high elevations in the Himalayan orogen since the middle Miocene
Authors: Long Sean P. , Robinson Delores M.
Published: June 2021

Supplemental figures and tables that provide supporting data for the compiled cross sections and the measured parameters, as well as text that summarizes the tectonostratigraphic units on each cross section.
Supplemental Material: Mantle control on magmatic flare-ups in the southern Coast Mountains batholith, British Columbia
Authors: Cecil M.R. , al. et
Published: June 2021

Includes sample location information, whole rock geochemical data, and individual zircon trace element, Lu-Hf isotope, and O isotope data.
COVID-19-induced postural orthostatic tachycardia syndrome treated with ivabradine
Authors: O'Sullivan Jenna Stephanie , Lyne Andrew , Vaughan Carl J
Published: June 2021

A 22-year-old woman was referred with exertional dyspnoea and chest tightness 3 weeks following a diagnosis of COVID-19. Evaluation revealed a resting sinus tachycardia and criteria for postural orthostatic tachycardia syndrome were met. After non-pharmacological interventions failed to yield symptomatic improvement, ivabradine was commenced. This intervention was followed by a substantial improvement in the patient’s exercise tolerance and energy levels and an objective reduction in supine and standing heart rate.
Clinical management of immune-related adverse events following immunotherapy treatment in patients with non-small cell lung cancer
Authors: Green Hannah Elizabeth , Nieva Jorge
Published: June 2021

The advent of checkpoint blockade-based immunotherapy is rapidly changing the management of lung cancer. Whereas past anticancer drugs’ primary toxicity was hematologic, the newer agents have primarily autoimmune toxicity. Thus, it is no longer enough for oncology practitioners to be skilled only in hematology. They must also understand management of autoimmune conditions, leveraging the skills of the rheumatologist, endocrinologist and gastroenterologist in the process. Herein we describe the mechanism of action and toxicities associated with immune checkpoint blockade in patients with lung cancer and provide a framework for management of adverse events.
Junior doctors and conscientious objection to voluntary assisted dying: ethical complexity in practice
Authors: McDougall Rosalind J , White Ben P , Ko Danielle , Keogh Louise , Willmott Lindy
Published: June 2021

In jurisdictions where voluntary assisted dying (VAD) is legal, eligibility assessments, prescription and administration of a VAD substance are commonly performed by senior doctors. Junior doctors’ involvement is limited to a range of more peripheral aspects of patient care relating to VAD. In the Australian state of Victoria, where VAD has been legal since June 2019, all health professionals have a right under the legislation to conscientiously object to involvement in the VAD process, including provision of information about VAD. While this protection appears categorical and straightforward, conscientious objection to VAD-related care is ethically complex for junior doctors for reasons that are specific to this group of clinicians. For junior doctors wishing to exercise a conscientious objection to VAD, their dependence on their senior colleagues for career progression creates unique risks and burdens. In a context where senior colleagues are supportive of VAD, the junior doctor’s subordinate position in the medical hierarchy exposes them to potential significant harms: compromising their moral integrity by participating, or compromising their career progression by objecting. In jurisdictions intending to provide all health professionals with meaningful conscientious objection protection in relation to VAD, strong specific support for junior doctors is needed through local institutional policies and culture.
Comparative safety of percutaneous ventricular assist device and intra-aortic balloon pump in acute myocardial infarction-induced cardiogenic shock
Authors: Ullah Waqas , Zghouzi Mohamed , Mukhtar Maryam , Banisad Ali , Alhatemi Gaith , Sattar Yasar , Zahid Salman , Moussa Pacha Homam , Gardi Delair , Alraies M Chadi
Published: June 2021

Background

The relative safety of percutaneous left ventricular assist device (pVAD) and intra-aortic balloon pump (IABP) in patients with cardiogenic shock after acute myocardial infarction remain unknown.

Methods

Multiple databases were searched to identify articles comparing pVAD and IABP. An unadjusted OR was used to calculate hard clinical outcomes and mortality differences on a random effect model.

Results

Seven studies comprising 26 726 patients (1110 in the pVAD group and 25 616 in the IABP group) were included. The odds of all-cause mortality (OR 0.57, 95% CI 0.47 to 0.68, p=<0.00001) and need for revascularisation (OR 0.16, 95% CI, 0.07 to 0.38, p=<0.0001) were significantly reduced in patients receiving pVAD compared with IABP. The odds of stroke (OR 1.12, 95% CI 0.14 to 9.17, p=0.91), acute limb ischaemia (OR=2.48, 95% CI 0.39 to 15.66, p=0.33) and major bleeding (OR 0.36, 95% CI 0.01 to 25.39, p=0.64) were not significantly different between the two groups. A sensitivity analysis based on the exclusion of the study with the largest weight showed no difference in the mortality difference between the two mechanical circulatory support devices.

Conclusions

In patients with acute myocardial infarction complicated by cardiogenic shock, there is no significant difference in the adjusted risk of all-cause mortality, major bleeding, stroke and limb ischaemia between the devices. Randomised trials are warranted to investigate further the safety and efficacy of these devices in patients with cardiogenic shock.
Orbifold black holes
Authors: Nitta Muneto , Uzawa Kunihito
Published: June 2021

We construct a regular black hole solution on the orbifold $${\mathbb C}^{n}/{\mathbb Z}_{n}$$

C

n

/

Z
n

in the ($$2n+1$$

2
n
+
1

)-dimensional Einstein–Maxwell theory. The event horizon is $$S^{2n-1}/{\mathbb Z}_{n}$$

S

2
n
-
1

/

Z
n

.
Synthesising Privacy by Design Knowledge Toward Explainable Internet of Things Application Designing in Healthcare
Authors: Alkhariji Lamya , Alhirabi Nada , Alraja Mansour Naser , Barhamgi Mahmoud , Rana Omer , Perera Charith
Published: June 2021

Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This article lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context—this approach is defined as
“Explainable Privacy.”
A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in Reference [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this article identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers’ capabilities at the design phase.
A Multi-instance Multi-label Dual Learning Approach for Video Captioning
Authors: Ji Wanting , Wang Ruili
Published: June 2021

Video captioning is a challenging task in the field of multimedia processing, which aims to generate informative natural language descriptions/captions to describe video contents. Previous video captioning approaches mainly focused on capturing visual information in videos using an encoder-decoder structure to generate video captions. Recently, a new encoder-decoder-reconstructor structure was proposed for video captioning, which captured the information in both videos and captions. Based on this, this article proposes a novel multi-instance multi-label dual learning approach (MIMLDL) to generate video captions based on the encoder-decoder-reconstructor structure. Specifically, MIMLDL contains two modules: caption generation and video reconstruction modules. The caption generation module utilizes a lexical fully convolutional neural network (Lexical FCN) with a weakly supervised multi-instance multi-label learning mechanism to learn a translatable mapping between video regions and lexical labels to generate video captions. Then the video reconstruction module synthesizes visual sequences to reproduce raw videos using the outputs of the caption generation module. A dual learning mechanism fine-tunes the two modules according to the gap between the raw and the reproduced videos. Thus, our approach can minimize the semantic gap between raw videos and the generated captions by minimizing the differences between the reproduced and the raw visual sequences. Experimental results on a benchmark dataset demonstrate that MIMLDL can improve the accuracy of video captioning.
Developing a “dry lab” activity using lower body negative pressure to teach physiology
Authors: Goswami Nandu , Singh Akanksha , Deepak Kishore Kumar
Published: June 2021

In this paper we assessed how lower body negative pressure (LBNP) can be used to teach students the physiological effects of central hypovolemia in the absence of the LBNP and/or a medical monitor using a “dry lab” activity using LBNP data that have been previously collected. This activity was performed using published LBNP papers, with which students could explore LBNP as an important tool to study physiological responses to central hypovolemia as well as consider issues in performing an LBNP experiment and interpreting experimental results. The activity was performed at the All India Institute of Medical Sciences, New Delhi, with 31 graduate students and 4 teachers of physiology. Both students and teachers were provided with a set of questionnaires that inquired about aspects related to the structure of the activity and how this activity integrated research and knowledge, as well as aspects related to motivation of the students and teachers to perform the activity. Our results from student and teacher surveys suggest that a “dry lab” activity using LBNP to teach physiology can be an important tool to expose students to the basics of systems physiology as well as to provide useful insights into how research is performed. Providing insight into research includes formulating a research question and then designing (including taking into account confounding variables), implementing, conducting, and interpreting research studies. Finally, developing such an activity using LBNP can also serve as a basis for developing research capacities and interests of students even early in their medical studies.
Some Improved Correlation Coefficients for q-Rung Orthopair Fuzzy Sets and Their Applications in Cluster Analysis
Authors: Bashir Huma , Inayatullah Syed , Alsanad Ahmed , Anjum Rukhshanda , Mosleh Mogeeb , Ashraf Pakeeza
Published: June 2021

The structure of q-rung orthopair fuzzy sets (q-ROFSs) is a generalization of fuzzy sets (FSs), intuitionistic FSs (IFSs), and Pythagorean FSs (PFSs). The notion of q-ROFSs has the proficiency of coping with uncertainty without any restrictions. In addition, the structure of q-ROFSs can effectively cope with the situations involving dual opinions without any restrictions, instead of dealing with only single opinion or dual opinions under certain restrictions. In clustering problems, the correlation coefficients are worthwhile because they provide the degree of similarity or correlation between two elements or sets. The theme of this study is to formulate the correlation coefficients for q-ROFSs that are basically the generalization of correlation coefficients of IFSs and PFSs. Moreover, an application of these correlation coefficients to a clustering problem is proposed. Also, an analysis of the outcomes is carried out. Furthermore, a comparison is carried out among the correlation coefficients for q-ROFSs and the existing ones. Finally, the downsides of the existing works and benefits of the correlation coefficients for q-ROFSs are discussed.
Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI
Authors: Naseer Asma , Yasir Tahreem , Azhar Arifah , Shakeel Tanzeela , Zafar Kashif
Published: June 2021

Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.
Visible Light-Induced Antibacterial Activity of Pigments Extracted from Dregs of Green and Black Teas
Authors: Indrawati Renny , Zubaidah Elok , Sutrisno Aji , Limantara Leenawaty , Yusuf Melisa Megawati , Brotosudarmo Tatas Hardo Panintingjati
Published: June 2021

Chlorophyll and its derivatives are potential natural sensitizers frequently applied in antimicrobial photodynamic therapy. Chlorophyll derivatives are formed naturally during tea processing, but they do not contribute to the color of tea infusions and thus are presumably left in the tea dregs. The present study aimed to investigate (i) the chlorophyll remnants in the pigments recovered from dregs of green and black teas and (ii) the antibacterial activity of pigments extracted from the tea dregs upon illumination using a light-emitting diode (LED) as the light source. Pigment analysis using high-performance liquid chromatography (HPLC) revealed the presence of main degradation products of chlorophylls, such as pheophytin and its epimers, pyropheophytin, and pheophorbides. In vitro assays demonstrated significant reductions in the number of viable bacteria in the presence of the pigments after 30 min of incubation with LED light irradiation. The descending order of bacterial susceptibility was Listeria monocytogenes > Staphylococcus aureus > Escherichia coli > Salmonella typhi. At an equivalent irradiation intensity, the blue and red LEDs could stimulate a comparable inactivation effect through photodynamic reactions. These findings demonstrated the valorization potential of tea dregs as a source of chlorophyll derivatives with visible light-induced antibacterial activity.
Translabyrinthine Petrous Apex Cholesteatoma Surgery with Hearing Preservation
Authors: Sudhoff Holger , Klingebiel Randolf , Scholtz Lars-Uwe , Todt Ingo
Published: June 2021

Objective. To introduce a novel surgical approach to petrous apex lesion (PA) with superior semicircular canal plugging for hearing preservation. Patient. A 63-year-old patient presented with a recurrent cholesteatoma of the left petrous apex. The patient had a long-term history of cholesteatoma and MRI with diffusion-weighted imaging (DWI) detected a suspicious lesion in the left petrous apex on follow-up. Intervention. The cholesteatoma could be completely removed from the petrous apex with partial superior semicircular canal plugging and removal with hearing preservation. Outcomes. Cholesteatomas of the temporal bone are managed by surgery with complete excision of the lesion. Results. The translabyrinthine approach, generally useful in nonhearing ears, could be utilized with the additional technique of superior semicircular canal plugging to preserve hearing in this patient. Conclusions. This case highlights the possibility of a hearing preservation strategy for PA cholesteatomas using a translabyrithine approach.
Antischistosomal Activity of Origanum majorana, Ziziphus spina-christi, and Salvia fruticosa Plant Extracts on Hamster Infected with Schistosoma haematobium
Authors: Fadladdin Yousef Abdal Jalil
Published: June 2021

World Health Organization (WHO) has approved only one treatment for schistosomiasis, praziquantel (PZQ), but some poor efficacy was noticed in patients during the early stage of infection. Therefore, researchers have intensified their efforts to research new alternative medicines to treat schistosomiasis. In the present study, in vitro as well as in vivo studies have been accomplished to evaluate the effect of Origanum majorana, Ziziphus spina-christi, and Salvia fruticosa extracts in a different concentration 500, 250, 125, 62.5, and 31.25 μg/ml on golden hamster infected by Egyptian strains of schistosome (Schistosoma haematobium). In vitro, the adult worms and schistosomula of S. haematobium were investigated in RPMI‐1640 medium for 48 hrs. The results showed that the concentration 500, 250, and 125 μg/ml of Origanum majorana, and Ziziphus spina-christi caused dead of 100% of Egyptian Schistosoma strains of adult worm and schistosomula of S. haematobium within 6 to 12 hrs of incubation. On the other hand, the extract of Salvia fruticosa at concentrations 500, 250, and 125 μg/ml showed death 100% parasites after 12 to 24 hrs of incubation. Inclusion, Origanum majorana, and Ziziphus spina-christi showed effectiveness against Egyptian Schistosoma strains (S. haematobium), a slight decrease in Salvia fruticosa was observed. Therefore, these medical plant extracts may be used as a safe and effective treatment for schistosomiasis.
Exploring Strategies for Improving Green Open Spaces in Old Downtown Residential Communities from the Perspective of Public Health to Enhance the Health and Well-Being of the Aged
Authors: Li Si-Jie , Luo Yu-Feng , Liu Zi-Chuan , Xiong Lei , Zhu Bo-Wei
Published: June 2021

Based on the trend of global aging, people are paying more and more attention to the health of the elderly and the improvement of green open spaces. However, few studies have focused on strategies to improve green spaces in response to this trend. Especially, with the outbreak of COVID-19, an urgent need to develop a sustainable system strategy to improve the health of the elderly in residential communities in old districts has emerged. Traditional improvement strategies based on current situation evaluation often focus on the most prominent practical problems. Therefore, the objective of this study was to provide theoretical research and practical improvement strategies for green open spaces in old downtown residential communities to improve the health and well-being of the elderly. In response to this problem, this research proposes an alternative method based on causality (FDM-DANP-mV model), by extracting 23 green open space elements that affect the health of the elderly and dividing them into three dimensions, to form a preliminary evaluation framework. On this basis, the more effective and feasible standard elements are screened out, and the influence relationship behind the elements is clarified. Then, the sustainable development strategy is systematically discussed in three practical cases. This allows for the analysis of the present situation to not only identify the current significant problems but also to capture the source of the influence behind the real problems based on the clarification of the dominant influence relationship. The actual value of this study is to provide a key design decision basis for the improvement of the green open spaces in old downtown residential communities, aiming at avoiding waste to the greatest extent under the premise of limited resources and gradually promoting the improvement of the urban built environment to promote the health and well-being of the elderly.
SD Simulation Research on the Green Low‐Carbon Development of Coal Enterprises
Authors: Xu Yinna , Zhao Guohao , Zhang Baojian , Jiao Jiao
Published: June 2021

The green low‐carbon development system of enterprises, differing from the traditional linear system, is a nonlinear feedback system with complex causality. Based on self‐organization theory, this study clarified the self‐organization evolution logic of the green low‐carbon development of coal enterprises and constructed a system dynamics model following a system dynamics method. Through a scenario simulation analysis, the impact of the market environment, environmental regulation, and enterprises’ innovation level on the green low‐carbon development of coal enterprises was revealed. Applicability suggestions based on simulation results were proposed. The results confirmed that the main challenge for coal enterprises is that the coal market environment restricts the promotion of green low‐carbon development level of coal enterprises. Improving innovation levels is the most effective way for coal enterprises to address these issues.
Multiscale Feature Learning Based on Enhanced Feature Pyramid for Vehicle Detection
Authors: Nguyen Hoanh
Published: June 2021

Vehicle detection is a crucial task in autonomous driving systems. Due to large variance of scales and heavy occlusion of vehicle in an image, this task is still a challenging problem. Recent vehicle detection methods typically exploit feature pyramid to detect vehicles at different scales. However, the drawbacks in the design prevent the multiscale features from being completely exploited. This paper introduces a feature pyramid architecture to address this problem. In the proposed architecture, an improving region proposal network is designed to generate intermediate feature maps which are then used to add more discriminative representations to feature maps generated by the backbone network, as well as improving the computational cost of the network. To generate more discriminative feature representations, this paper introduces multilayer enhancement module to reweight feature representations of feature maps generated by the backbone network to increase the discrimination of foreground objects and background regions in each feature map. In addition, an adaptive RoI pooling module is proposed to pool features from all pyramid levels for each proposal and fuse them for the detection network. Experimental results on the KITTI vehicle detection benchmark and the PASCAL VOC 2007 car dataset show that the proposed approach obtains better detection performance compared with recent methods on vehicle detection.
A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells
Authors: Lilli L. , Giarnieri E. , Scardapane S.
Published: June 2021

Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains underexplored. In this paper, we propose a novel deep learning model for cancer detection from urinary cytopathology screening images. We leverage recent ideas from the field of multioutput neural networks to provide a model that can efficiently train even on small-scale datasets, such as those typically found in real-world scenarios. Additionally, we argue that calibration (i.e., providing confidence levels that are aligned with the ground truth probability of an event) has been a major shortcoming of prior works, and we experiment a number of techniques to provide a well-calibrated model. We evaluate the proposed algorithm on a novel dataset, and we show that the combination of focal loss, multiple outputs, and temperature scaling provides a model that is significantly more accurate and calibrated than a baseline deep convolutional network.
The Top 100 Cited Papers in the Field of Iron Deficiency in Humans: A Bibliometric Study
Authors: Frater John L.
Published: June 2021

Worldwide, iron deficiency is a common form of micronutrient deficiency with a high individual and societal cost. There are considerable knowledge and practice gaps in the diagnosis and treatment of iron deficiency. Bibliometric analysis examines the published body of knowledge of a subject in an objective fashion. The Web of Science Core Collection was searched to retrieve the 100 most cited papers on the topic of iron deficiency, and the key metrics of each paper were extracted. A keyword study was performed using VOSviewer 1.6.10 software, which provided a visual mapping of the network of keyword cooccurrences. The papers were published between 1964 and 2017 and were cited an average of 636 times. They were contributed by authors from 119 different countries/regions, with the largest contributing country being the United States. 29 institutions contributed at least 6 publications each, and 4 researchers authored or coauthored at least 5 papers. Keyword analysis suggests that the most cited topics could be grouped into 4 categories: (1) epidemiologic research of the global burden of iron deficiency, (2) clinical aspects of iron deficiency anemia, (3) iron metabolism, and (4) the impact of iron deficiency on children. Identification of the most impactful studies in the field of iron deficiency may be helpful to practitioners interested in improving their knowledge base. Compared to bibliometric studies performed on other topics, the medical literature of iron deficiency is mature, as evidenced by the high citation rate of the top 100 papers. Despite the high worldwide prevalence of iron deficiency, the top cited papers are dominated by a relatively small number of countries and institutions. Interestingly, however, the most cited authors in this study do not overlap with the most cited institutions.
The Effect of a Traditional Preparation Containing Piper nigrum L. and Bunium persicum (Boiss.) B.Fedtsch. on Immobility Stress‐Induced Memory Loss in Mice
Authors: Rashedinia Marzieh , Mojarad Mina , Khodaei Forouzan , Sahragard Ali , Khoshnoud Mohammad Javad , Zarshenas Mohammad M.
Published: June 2021

Objective. Alzheimer’s disease is a progressive, age‐related, and neurodegenerative disease characterized by mental decline. The exact cause of Alzheimer’s disease is unclear, but cholinergic dysfunction, protein accumulation, and oxidative stress are among the most important hypotheses. The main purpose of our study was to investigate the effects of aqueous and hydroalcoholic extract combination of these two medicinal plants, black pepper and cumin (as a related formulation in traditional Persian medicine), on memory and learning of an immobilized stress animal model. Methods. In this study, hydroalcoholic and aqueous extracts of cumin and black pepper fruits were prepared. Six groups of mice were treated orally for 2 weeks: control group, immobility stress, and stress‐induced immobility mice received different doses of the hydroalcoholic extract (100 and 200 mg/kg) and aqueous extract (100 and 200 mg/kg). The shuttle box, novel object detection, and rotarod test were used to evaluate memory and learning. The activities of acetylcholinesterase, catalase (CAT), and superoxide dismutase (SOD) and the level of reduced glutathione (GSH) and malondialdehyde (MDA) were measured in the brain tissue. Results. Immobility stress significantly reduced learning and motor coordination. Furthermore, MDA levels and acetylcholinesterase activity were significantly increased, while CAT and SOD activities were significantly reduced in the brain of immobility‐induced stress mice. Other findings indicated that hydroalcoholic and aqueous extracts (100 and 200 mg/kg) of cumin and black pepper fruits have an improving effect on animal motor coordination and learning ability, GSH content, and CAT, SOD, and acetylcholinesterase enzyme function in comparison with stress groups (p < 0.05). Conclusion. The hydroalcoholic and aqueous extracts of cumin and black pepper fruits have protective effects against stress‐induced memory deficit and oxidative stress and may have beneficial therapeutic effect in the treatment of neurodegenerative diseases.
Liver Enzymes and Their Association with Some Cardiometabolic Diseases: Evidence from a Large Kurdish Cohort
Authors: Kohsari Maryam , Moradinazar Mehdi , Rahimi Zohreh , Pasdar Yahya , Shakiba Ebrahim
Published: June 2021

Objective. According to reports, liver enzymes might play a role in the incidence and development of cardiometabolic diseases such as metabolic syndrome (MetS), hypertension (HTN), and cardiovascular diseases (CVD). We conducted a study to investigate this hypothesis among the Iranian Kurdish population. Methods. We analyzed data from the baseline phase of the Ravansar noncommunicable disease (RaNCD) cohort. The association between liver enzymes (ALT, AST, ALT/AST ratio, GGT, and ALP) with cardiometabolic disease risk factors was investigated by multiple linear regression. The odds ratio of cardiometabolic diseases in each quartile category of liver enzyme concentration was estimated using multivariable logistic regression. Results. The mean age of participants was 47.3 ± 4.1 years (48.1 years in males and 51.8 years in females). In the adjusted model, all enzymes were positively associated with MetS, HTN, and CVD risk factors except for the ALT/AST ratio with SBP and DBP. In the adjusted model, subjects in the fourth quartile for GGT, ALT/AST ratio, ALT, ALP, and AST had 3.29‐, 2.94‐, 2.45‐, 2.00‐, and 1.19‐fold increased risk for MetS compared with subjects in the first quartile. Increased levels of GGT and ALP were positively associated with the risk of HTN (ORs = 1.33, 95%CI = 1.03–1.71 for GGT; ORs = 1.32, 95%CI = –1.68 for ALP). An increased GGT level was significantly associated with CVD (ORs = 1.54, 95%CI = 1.03–1.68). Within the normal range quartile, ALT had a significant correlation with the incidence of MetS. Conclusion. According to the present study, the levels of liver enzymes could be considered for early diagnosis of MetS, HTN, and CVD.
Recognition of Imbalanced Epileptic EEG Signals by a Graph‐Based Extreme Learning Machine
Authors: Zhou Jie , Zhang Xiongtao , Jiang Zhibin
Published: June 2021

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph‐based extreme learning machine method (G‐ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.
Instability Condition Derivation for Hydraulic AGC System under Pressure Closed‐Loop Control
Authors: Zhu Yong , Li Guangpeng , Tang Shengnan , Jiang Wanlu , Qian Pengfei , Zheng Zhi , Zheng Zhijian
Published: June 2021

In strip rolling, hydraulic automatic gauge control (HAGC) system is the key element to guarantee the precision of strip gauge. The stability of the kernel pressure closed loop (PCL) in the HAGC system plays an essential role in guaranteeing the rolling process with high performance. Nevertheless, there is some difficulty in exploring the instability mechanism of the HAGC system due to the fact that the PCL is a representative nonlinear closed‐loop control system. In this work, for each component of the HAGC system, the mathematical model was established. And on the basis of the linking relation of various elements, we derived the incremental transfer model of the PCL system. Furthermore, in accordance with the deduced information transfer relation, the transfer block diagram of disturbing variable of the PCL system was obtained. Moreover, for the purpose of deriving the instability condition of the PCL system, the Popov frequency criterion was employed. The instability conditions of the HAGC system were obtained under PCL control. Furthermore, the derived instability conditions of the HAGC system were experimentally verified under various working conditions. The research results provide a fundamental foundation for studying the instability mechanism of the HAGC system.
Mangiferin Alleviates Mitochondrial ROS in Nucleus Pulposus Cells and Protects against Intervertebral Disc Degeneration via Suppression of NF‐κB Signaling Pathway
Authors: Yu Haichao , Hou Guowei , Cao Jiankang , Yin Yanyu , Zhao Yunpeng , Cheng Lei
Published: June 2021

Intervertebral disc degeneration (IVDD), one of the most common clinical diseases worldwide, causes disc herniation and sciatica. Recent studies have identified the involvement of mitochondrial dysfunction, inflammatory responses, and extracellular matrix degradation in IVDD. Mangiferin is known to protect against various diseases by inhibiting oxidative stress, suppressing inflammation reaction, and relieving mitochondrial dysfunction. Whether mangiferin can alleviate IVDD remains to be elucidated. In the present study, human nucleus pulposus cells (HNPCs) and mouse intervertebral discs were cultured and stimulated with TNF‐α, with or without treatment of mangiferin. Moreover, we established a rat needle puncture model and injected mangiferin into the intervertebral discs to verify its protective effect on IVDD. Furthermore, the activity of the NF‐κB signaling pathway was tested in vitro. Our results indicated that mangiferin alleviated the inflammatory response and reversed the loss of major intervertebral disc components. Besides, mangiferin reduced reactive oxygen species production, ameliorated mitochondrial damage, and decreased the expression of apoptosis‐related parameters in stimulation of TNF‐α. In addition, mangiferin antagonized the activation of the NF‐κB signaling pathway induced by TNF‐α. Collectively, mangiferin antagonized mitochondrial ROS in NP cells and protected against IVDD by suppressing the activation of the NF‐κB signaling pathway, which might provide a potential therapeutic instrument for IVDD.
Logarithmic Coefficient Bounds and Coefficient Conjectures for Classes Associated with Convex Functions
Authors: Alimohammadi Davood , Analouei Adegani Ebrahim , Bulboacă Teodor , Cho Nak Eun
Published: June 2021

It is well-known that the logarithmic coefficients play an important role in the development of the theory of univalent functions. If

S

denotes the class of functions

f

z

=
z
+

∑

n
=
2

∞

a

n

z

n

analytic and univalent in the open unit disk

U

, then the logarithmic coefficients

γ

n

f

of the function

f
∈
S

are defined by

log

f

z

/
z

=
2

∑

n
=
1

∞

γ

n

f

z

n

. In the current paper, the bounds for the logarithmic coefficients

γ

n

for some well-known classes like

C

1
+
α
z

for

α
∈

0
,
1

and

C

V

hpl

1
/
2

were estimated. Further, conjectures for the logarithmic coefficients

γ

n

for functions

f

belonging to these classes are stated. For example, it is forecasted that if the function

f
∈
C

1
+
α
z

, then the logarithmic coefficients of

f

satisfy the inequalities

γ

n

≤
α
/

2
n

n
+
1

,
n
∈
ℕ
.

Equality is attained for the function

L

α
,
n

, that is,

log

L

α
,
n

z

/
z

=
2

∑

n
=
1

∞

γ

n

L

α
,
n

z

n

=

α
/
n

n
+
1

z

n

+
⋯
,
z
∈
U
.
Extremal Values of Randić Index among Some Classes of Graphs
Authors: Ghalavand Ali , Ashrafi Ali Reza , Pourbabaee Marzieh
Published: June 2021

Suppose

G

is a simple graph with edge set

E

G

. The Randić index

R

G

is defined as

R

G

=

∑

u
v
∈
E

G

1
/

deg

G

u

deg

G

v

, where

deg

G

u

and

deg

G

v

denote the vertex degrees of

u

and

v

in

G

, respectively. In this paper, the first and second maximum of Randić index among all

n
−

vertex

c
−

cyclic graphs was computed. As a consequence, it is proved that the Randić index attains its maximum and second maximum on two classes of chemical graphs. Finally, we will present new lower and upper bounds for the Randić index of connected chemical graphs.
Diffuse Gonococcal Infection (DGI) in a Patient with Treatment-Refractory Acetylcholine Receptor Antibody-Positive (AChR+) Generalized Myasthenia Gravis (gMG) Treated with Eculizumab
Authors: Katyal Nakul , Nirola Latika , Narula Naureen , Govindarajan Raghav
Published: June 2021

Patients receiving complement inhibitor, eculizumab, are at high risk for infections with encapsulated organisms such as Neisseria due to impaired opsonophagocytic activity. Impaired complement immunity may increase the risk for dissemination of asymptomatic Neisseria gonorrhoeae. Disseminated Gonococcal Infection (DGI) is a rare but potentially life-threatening complication associated with eculizumab. Physicians should obtain adequate sexual histories from the patients and educate them on safe sexual practices. Here, we describe a case of DGI in a 32-year-old African American female patient with acetylcholine receptor antibody-positive (AChR+) generalized myasthenia gravis (gMG), receiving eculizumab.
Computation of Topological Indices of NEPS of Graphs
Authors: Imran Muhammad , Akhter Shehnaz , Jamil Muhammad Kamran
Published: June 2021

The inspection of the networks and graphs through structural properties is a broad research topic with developing significance. One of the methods in analyzing structural properties is obtaining quantitative measures that encode data of the whole network by a real quantity. A large quantity of graph‐associated numerical invariants has been used to examine the whole structure of networks. In this analysis, degree‐related topological indices have a significant place in nanotechnology and theoretical chemistry. Thereby, the computation of indices is one of the successful branches of research. The noncomplete extended p‐sum (NEPS) of graphs is a famous general graph product. In this paper, we investigated the exact formulas of general zeroth‐order Randić, Randić, and the first multiplicative Zagreb indices for NEPS of graphs.
Development and Optimization for a New Planar Spring Using Finite Element Method, Deep Feedforward Neural Networks, and Water Cycle Algorithm
Authors: Le Chau Ngoc , Le Hieu Giang , Dang Van Anh , Dao Thanh-Phong
Published: June 2021

The gravity balance mechanism plays a vital role in maintaining the equilibrium for robots and assistive devices. The purpose of this paper was to optimize the geometry of a planar spring, which is an essential element of the gravity balance mechanism. To implement the optimization process, a hybrid method is proposed by combining the finite element method, the deep feedforward neural network, and the water cycle algorithm. Firstly, datasets are collected using the finite element method with a full experiment design. Secondly, the output datasets are normalized to eliminate the effects of the difference of units. Thirdly, the deep feedforward neural network is then employed to build the approximate models for the strain energy, deformation, and stress of the planar spring. Finally, the water cycle algorithm is used to optimize the dimensions of the planar spring. The results found that the optimal geometries of the spring include the length of 45 mm, the thickness of 1.029 mm, the width of 9 mm, and the radius of 0.3 mm. Besides, the predicted results determined that the strain energy, the deformation, and the stress are 0.01123 mJ, 33.666 mm, and 79.050 MPa, respectively. The errors between the predicted result and the verifying results for the strain energy, the deformation, and the stress are about 1.87%, 1.69%, and 3.06%, respectively.
Development and Application of Medicine‐Engineering Integration in the Rehabilitation of Traumatic Brain Injury
Authors: Wang Qingyong , Sun Weibo , Qu Yuanyuan , Feng Chuwen , Wang Delong , Yin Hongna , Li Chaoran , Sun Zhongren , Sun Dongwei
Published: June 2021

The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At present, these techniques has been widely used in the rehabilitation of TBI. In this review article, we summarizes the progress of the combination of medicine and industry in the rehabilitation of traumatic brain injury in recent years, mainly from the following aspects: artificial intelligence (AI), brain‐computer interfaces (BCI), noninvasive brain stimulation (NIBS), and wearable‐assisted devices. We believe the summary of this article can improve insight into the combination of medicine and industry in the rehabilitation of traumatic brain injury.
TP53 Disruption in Chronic Lymphocytic Leukemia Under Ibrutinib: More is Worse?
Authors: Bomben Riccardo , Zucchetto Antonella , Gentile Massimo , Gattei Valter
Published: June 2021

Patients with chronic lymphocytic leukemia carrying a single TP53 hit (chromosome 17p deletion or single TP53 mutation) demonstrate excellent progression-free survival and overall survival on ibrutinib compared with cases harboring multiple TP53 hits. Testing TP53 deletion/mutation combining FISH and deep next-generation sequencing should be performed for a correct patient evaluation.

See related article by Brieghel et al., p. 4531