bluuebunny/crossref_metadata_2025 huggingface dataset:
An Overview of Antimicrobial Stewardship Optimization: The Use of Antibiotics in Humans and Animals to Prevent Resistance
Authors: Rahman Md. Mominur , Alam Tumpa Mst. Afroza , Zehravi Mehrukh , Sarker Md. Taslim , Yamin Md. , Islam Md. Rezaul , Harun-Or-Rashid Md. , Ahmed Muniruddin , Ramproshad Sarker , Mondal Banani , Dey Abhijit , Damiri Fouad , Berrada Mohammed , Rahman Md. Habibur , Cavalu Simona
Published: May 2022

Antimicrobials are a type of agent widely used to prevent various microbial infections in humans and animals. Antimicrobial resistance is a major cause of clinical antimicrobial therapy failure, and it has become a major public health concern around the world. Increasing the development of multiple antimicrobials has become available for humans and animals with no appropriate guidance. As a result, inappropriate use of antimicrobials has significantly produced antimicrobial resistance. However, an increasing number of infections such as sepsis are untreatable due to this antimicrobial resistance. In either case, life-saving drugs are rendered ineffective in most cases. The actual causes of antimicrobial resistance are complex and versatile. A lack of adequate health services, unoptimized use of antimicrobials in humans and animals, poor water and sanitation systems, wide gaps in access and research and development in healthcare technologies, and environmental pollution have vital impacts on antimicrobial resistance. This current review will highlight the natural history and basics of the development of antimicrobials, the relationship between antimicrobial use in humans and antimicrobial use in animals, the simplistic pathways, and mechanisms of antimicrobial resistance, and how to control the spread of this resistance.
The Protective Effect of Mulberry Leaf Flavonoids on High-Carbohydrate-Induced Liver Oxidative Stress, Inflammatory Response and Intestinal Microbiota Disturbance in Monopterus albus
Authors: Shi Yong , Zhong Lei , Fan Yuding , Zhang Junzhi , Zhong Huan , Liu Xiang , Shao Chuang , Hu Yi
Published: May 2022

An 8-week feeding trial with high-carbohydrate- and 100, 200 and 300 mg/kg mulberry leaf flavonoids (MLF)-supplemented diets (HCF1, HCF2 and HCF3, respectively) was conducted to evaluate the protective effect of MLF on oxidized high-carbohydrate-induced glucose metabolism disorder, liver oxidative damage and intestinal microbiota disturbance in Monopterus albus. The results showed that HC diets had significant negative effects on growth, glucose metabolism, liver antioxidant and immunity, as well as intestinal microbiota, in comparison to CON diets. However, WGR and SR in the HCF3 group dramatically increased compared to the HC group. With the increase of MLF in the HC diet, the activities of glycolysis and antioxidant enzymes in the liver tended to increase, while the changes of gluconeogenesis-related enzyme activities showed the opposite trend and significantly changed in the HCF3 group. Additionally, MLF supplementation dramatically increased the mRNA expression involved in glycolysis, antioxidative enzymes and anti-inflammatory cytokines in comparison with the HC group. Furthermore, gluconeogenesis and pro-inflammatory cytokine genes’ expression dramatically decreased. Furthermore, the proportion of Clostridium and Rhodobacter in the HC group dramatically declined, and the proportion of Lactococcus dramatically increased, compared to the HC group. In addition, 300 mg/kg MLF supplementation significantly improved the species composition and homeostasis of intestinal microbiota. These results indicate that MLF can alleviate the negative effects of low growth performance, glucose metabolism disorder, liver oxidative damage and intestinal microbiota disturbance caused by HC diets, and the relief of MLF is dose-related.
A Dense Feature Pyramid Network for Remote Sensing Object Detection
Authors: Sun Yu , Liu Wenkai , Gao Yangte , Hou Xinghai , Bi Fukun
Published: May 2022

In recent years, object detection in remote sensing images has become a popular topic in computer vision research. However, there are various problems in remote sensing object detection, such as complex scenes, small objects in large fields of view, and multi-scale object in different categories. To address these issues, we propose DFPN-YOLO, a dense feature pyramid network for remote sensing object detection. To address difficulties in detecting small objects in large scenes, we add a larger detection layer on top of the three detection layers of YOLOv3, and we propose Dense-FPN, a dense feature pyramid network structure that enables all four detection layers to combine semantic information before sampling and after sampling to improve the performance of object detection at different scales. In addition, we add an attention module in the residual blocks of the backbone to allow the network to quickly extract key feature information in complex scenes. The results show that the mean average precision (mAP) of our method on the RSOD datasets reached 92%, which is 8% higher than the mAP of YOLOv3, and the mAP increased from 62.41% on YOLOv3 to 69.33% with our method on the DIOR datasets, outperforming even YOLOv4.
Model of Emotion Judgment Based on Features of Multiple Physiological Signals
Authors: Lin Wenqian , Li Chao , Zhang Yunmian
Published: May 2022

The model of emotion judgment based on features of multiple physiological signals was investi-gated. In total, 40 volunteers participated in the experiment by playing a computer game while their physiological signals (skin electricity, electrocardiogram (ECG), pulse wave, and facial electromy-ogram (EMG)) were acquired. The volunteers were asked to complete an emotion questionnaire where six typical events that appeared in the game were included, and each volunteer rated their own emotion when experiencing the six events. Based on the analysis of game events, the signal data were cut into segments and the emotional trends were classified. The correlation between data segments and emotional trends was built using a statistical method combined with the questionnaire responses. The set of optimal signal features was obtained by processing the data of physiological signals, extracting the features of signal data, reducing the dimensionality of signal features, and classifying the emotion based on the set of signal data. Finally, the model of emotion judgment was established by selecting the features with a significance of 0.01 based on the correlation between the features in the set of optimal signal features and emotional trends.
Design and Experimental Verification on Performance of a Novel Integrated Electro-Hydraulic Vane Pump
Authors: Yang Weiwei , Wang Yilin , Yang Yaodong , Zhang Wenming
Published: May 2022

The integrated electric-hydraulic pump has the advantages of no leakage, compact structure, and low noise. Here, we propose a novel integrated electric-hydraulic vane pump (IEHVP) to solve the problem of low reliability of the existing ordinary axially arranged hydraulic oil source in the harsh deep-sea environments. IEVP combines an external-rotor brushless motor and a balanced vane pump. In order to obtain the external characteristic curve of the designed IEHVP, the theoretical analysis of coupling analysis between the operating characteristics of the electric motor and the vane pump is designed. The results reveal that the output flow of the proposed IEHVP increases linearly with the increase of the input voltage and decreases non-linearly as the outlet pressure increase. Finally, the proposed IEHVP is tested. The relationship between the output flow and volumetric efficiency and its outlet pressure and input voltage is obtained. The experimental results verify the characteristics of the proposed IEHVP design.
Non-Isolated DC-DC Converters in Fuel Cell Applications: Thermal Analysis and Reliability Comparison
Authors: Alavi Omid , Rajabloo Talieh , De Ceuninck Ward , Daenen Michaël
Published: May 2022

An alternative energy source that has appeared beyond expectations and has seen a lot of progress is the fuel cell. A proton exchange membrane (PEM) fuel cell is chosen for analysis and requires a DC-DC boost converter as an interface between the fuel cell and the load to provide a high-gain regulated voltage. Although great effort towards developing different converter topologies has been made during recent decades, less attention has been devoted to the reliability and thermal performance assessment of the present converters. In this paper, five non-isolated DC-DC converters are analyzed in terms of both thermal behavior and reliability. The temperature estimation of semiconductor devices as a critical part of the thermal analysis has been made via a detailed thermal model and the reliability is evaluated by means of a power cycling test. Finally, a performance score has been attributed using the TOPSIS ranking methodology and considering all the criteria (e.g., the number of components and cost) at the same time. The results indicated that the floating interleaved boost converter is always at the top of the list, even if the weight of the indicators is changed. When the weight of the cost criterion is higher than the reliability criterion, the multi-switch boost converter will be in second place. If the weight of the reliability criterion is greater than cost, the interleaved and multi-switch converter are ranked second and third, respectively. Additionally, the Cuk converter with a closeness coefficient of zero is always associated with the most unfavorable performance.
Parallel Bidirectionally Pretrained Taggers as Feature Generators
Authors: Stanković Ranka , Škorić Mihailo , Šandrih Todorović Branislava
Published: May 2022

In a setting where multiple automatic annotation approaches coexist and advance separately but none completely solve a specific problem, the key might be in their combination and integration. This paper outlines a scalable architecture for Part-of-Speech tagging using multiple standalone annotation systems as feature generators for a stacked classifier. It also explores automatic resource expansion via dataset augmentation and bidirectional training in order to increase the number of taggers and to maximize the impact of the composite system, which is especially viable for low-resource languages. We demonstrate the approach on a preannotated dataset for Serbian using nested cross-validation to test and compare standalone and composite taggers. Based on the results, we conclude that given a limited training dataset, there is a payoff from cutting a percentage of the initial training set and using it to fine-tune a machine-learning-based stacked classifier, especially if it is trained bidirectionally. Moreover, we found a measurable impact on the usage of multiple tagsets to scale-up the architecture further through transfer learning methods.
Ongoing Decline in the Atmospheric COS Seasonal Cycle Amplitude over Western Europe: Implications for Surface Fluxes
Authors: Belviso Sauveur , Remaud Marine , Abadie Camille , Maignan Fabienne , Ramonet Michel , Peylin Philippe
Published: May 2022

Atmospheric carbonyl sulfide (COS) was monitored at the GIF site (France) from August 2014 to November 2021. A significant decreasing trend in the seasonal cycle amplitude (SCA) of the COS was observed for the first time in the Northern Hemisphere (−27 ppt over 6 years). The lowest SCA was recorded in 2021 (80 ppt vs. 107 ppt in 2015). The trend in the SCA results revealed a steeper decline in the spring maximum than in that of the autumn minimum (−49 ppt vs. −10 ppt over 6 years, respectively). These negative trends were qualitatively consistent with those in the tropospheric COS put forward by the NDACC network of ground-based FTIR instruments, which were attributed to a slowing in the rate of COS anthropogenic emissions. Simulations using the ORCHIDEE land-surface model showed that a decrease in COS lowers the uptake of this gas by plants. Our observations suggest the existence of a causal relationship between the decline in the SCA and that in the tropospheric COS, implying that the temporal variations in the COS SCA over Western Europe are essentially driven by plant uptake. However, the transport by the LMDz 3-D model of surface fluxes for each component of the COS budget failed to reproduce this feature at GIF, pointing to a likely misrepresentation of the marine and anthropogenic fluxes in the footprint of this station.
Spatiotemporal Epidemiology of Tuberculosis in Thailand from 2011 to 2020
Authors: Chinpong Kawin , Thavornwattana Kaewklao , Armatrmontree Peerawich , Chienwichai Peerut , Lawpoolsri Saranath , Silachamroon Udomsak , Maude Richard J. , Rotejanaprasert Chawarat
Published: May 2022

Tuberculosis is a leading cause of infectious disease globally, especially in developing countries. Better knowledge of spatial and temporal patterns of tuberculosis burden is important for effective control programs as well as informing resource and budget allocation. Studies have demonstrated that TB exhibits highly complex dynamics in both spatial and temporal dimensions at different levels. In Thailand, TB research has been primarily focused on surveys and clinical aspects of the disease burden with little attention on spatiotemporal heterogeneity. This study aimed to describe temporal trends and spatial patterns of TB incidence and mortality in Thailand from 2011 to 2020. Monthly TB case and death notification data were aggregated at the provincial level. Age-standardized incidence and mortality were calculated; time series and global and local clustering analyses were performed for the whole country. There was an overall decreasing trend with seasonal peaks in the winter. There was spatial heterogeneity with disease clusters in many regions, especially along international borders, suggesting that population movement and socioeconomic variables might affect the spatiotemporal distribution in Thailand. Understanding the space-time distribution of TB is useful for planning targeted disease control program activities. This is particularly important in low- and middle-income countries including Thailand to help prioritize allocation of limited resources.
Compositional Shifts in Microbial Diversity under Traditional Banana Cropping Systems of Sub-Saharan Africa
Authors: Kaushal Manoj , Tumuhairwe John Baptist , Kaingo Jacob , Richard Malingumu , Nakamanya Florence , Taulya Godfrey , Coyne Danny
Published: May 2022

Improvements in the crop productivity, soil health, and sustainable intensification should be premised on the better understanding of interactions between the cropping systems and soil microbial diversity. In this study, we assessed variations in the microbial communities across the traditional banana-based cropping systems of contrasting monocrop vigor (vigorous or V vs. non-vigorous or NV) and the cropping system (monocrop or MC vs. intercropped or IC) using 16S rDNA (V3–V4) and ITS2 amplicon deep sequencing via Illumina platform. Sequencing results of the bacterial and fungal communities showed high variability among MC and V cropping systems. The abundances of Proteobacteria, Firmicutes, and Actinobacteria were significantly higher in NV (non-vigorous) and V (vigorous) cropping systems; and the abundances of Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes in the MC (monocropping) than IC (intercropping). There were high relative abundances of Pseudomonas (6.1–37.43%), Bacillus (4.5–20.4%), Rhizobium (1.4–6.5%), and Devosia (1.5–6.7%) in the cropping systems. The dominant family of fungal class Incertae_sedis was Mortierellales, which accounted for 8.79–41.12% of total taxa. This result indicated that the cropping systems are vital for supporting the dynamic microbial diversity specifically beneficial for bacterial communities that helps in promoting synergistic plant-soil interactions and total productivity under resource poor conditions of smallholder farmers in sub-Saharan Africa (SSA).
Satisfaction and Demands of Indoor Space in the High-Density Residential Areas in the COVID-19 Era
Authors: Yang Jing , Xu Jiahang , Hu Tingting , Cao Jianing
Published: May 2022

The last few years have witnessed a change in residents’ demand for indoor space due to the COVID-19 pandemic. From the perspective of residential satisfaction in the urban areas in various levels of COVID-19 severity, the household survey was conducted to explore the changing residential demands. The IBM SPSS Statistics was employed to analyze the survey data with a focus on the relationship between pandemic severity and residents’ satisfaction, as well as the future influence of COVID-19 on indoor space and the varying demands. Correlation analysis was performed. The variables included in the correlation analysis were the following: urban epidemic severity, number of confirmed cases, density of confirmed cases, regional pandemic severity and satisfaction of different indoor spaces. This study revealed that the hallway, bathroom, living room and master bedroom are key areas in which the residential demands are concentrated. These should be paid attention to in the future residential design.
Study on Optimal Design of Grotto-Eave System with Cable Inerter Viscous Damper for Vibration Control
Authors: Huang Jizhong , Zhang Ruoyu , Luo Qingyang , Guo Xiuwei , Cao Meigen
Published: May 2022

In this paper, the mechanical model of grotto–eave system with cable inerter viscous damper (CIVD) is established, and the vibration control equations are established. Firstly, the stochastic response is carried out, and the optimization design of design parameters of CIVD is carried out for the grotto–eave systems with different connection types. Finally, the vibration mitigation control performance of CIVD under different seismic inputs is analyzed. The research shows that in the optimal design of CIVD, the inerter–mass ratio and damping ratio should be reduced as much as possible to improve the feasibility of the application of CIVD in cultural relics protection engineering under the condition of meeting the target damping ratio. The demand-based optimal method can minimize the cost by enhancing damping element deformation in a small damping ratio, while ensuring that the value of displacement index of grotto–eave system can be reached. Hence, the deformation and damping force of CIVD will increase simultaneously due to the efficient tuning and damping amplification of CIVD. CIVD can enlarge the apparent mass through rotation and damping force through enhancement deformation. Hence, compared with other conventional dampers (such as viscous damper), optimal CIVD has lower damping ratio under the same demand index of grotto–eave system. It can be realized that the lightweight and high efficiency of the damper, and can be applied to the vibration mitigation and reinforcement of the grotto–eave system.
Acquired αSMA Expression in Pericytes Coincides with Aberrant Vascular Structure and Function in Pancreatic Ductal Adenocarcinoma
Authors: Natarajan Vikneshwari , Ha Sangdeuk , Delgado Alexander , Jacobson Reed , Alhalhooly Lina , Choi Yongki , Kim Jiha
Published: May 2022

The subpopulations of tumor pericytes undergo pathological phenotype switching, affecting their normal function in upholding structural stability and cross-communication with other cells. In the case of pancreatic ductal adenocarcinoma (PDAC), a significant portion of blood vessels are covered by an α-smooth muscle actin (αSMA)-expressing pericyte, which is normally absent from capillary pericytes. The DesminlowαSMAhigh phenotype was significantly correlated with intratumoral hypoxia and vascular leakiness. Using an in vitro co-culture system, we demonstrated that cancer cell-derived exosomes could induce ectopic αSMA expression in pericytes. Exosome-treated αSMA+ pericytes presented altered pericyte markers and an acquired immune-modulatory feature. αSMA+ pericytes were also linked to morphological and biomechanical changes in the pericyte. The PDAC exosome was sufficient to induce αSMA expression by normal pericytes of the healthy pancreas in vivo, and the vessels with αSMA+ pericytes were leaky. This study demonstrated that tumor pericyte heterogeneity could be dictated by cancer cells, and a subpopulation of these pericytes confers a pathological feature.
Diagnostic Validation of a Comprehensive Targeted Panel for Broad Mutational and Biomarker Analysis in Solid Tumors
Authors: Froyen Guy , Geerdens Ellen , Berden Severine , Cruys Bert , Maes Brigitte
Published: May 2022

The use of targeted Next Generation Sequencing (NGS) for the diagnostic screening of somatic variants in solid tumor samples has proven its high clinical value. Because of the large number of ongoing clinical trials for a multitude of variants in a growing number of genes, as well as the detection of proven and emerging pan-cancer biomarkers including microsatellite instability (MSI) and tumor mutation burden (TMB), the currently employed diagnostic gene panels will become vastly insufficient in the near future. Here, we describe the validation and implementation of the hybrid capture-based comprehensive TruSight Oncology (TSO500) assay that is able to detect single-nucleotide variants (SNVs) and subtle deletions and insertions (indels) in 523 tumor-associated genes, copy-number variants (CNVs) of 69 genes, fusions with 55 cancer driver genes, and MSI and TMB. Extensive validation of the TSO500 assay was performed on DNA or RNA from 170 clinical samples with neoplastic content down to 10%, using multiple tissue and specimen types. Starting with 80 ng DNA and 40 ng RNA extracted from formalin-fixed and paraffine-embedded (FFPE) samples revealed a precision and accuracy >99% for all variant types. The analytical sensitivity and specificity were at least 99% for SNVs, indels, CNVs, MSI, and gene rearrangements. For TMB, only values around the threshold could yield a deviating outcome. The limit-of-detection for SNVs and indels was well below the set threshold of 5% variant allele frequency (VAF). This validated comprehensive genomic profiling assay was then used to screen 624 diagnostic samples, and its success rate for adoption in a clinical diagnostic setting of broad solid tumor screening was assessed on this cohort.
Leveraging STING, Batf3 Dendritic Cells, CXCR3 Ligands, and Other Components Related to Innate Immunity to Induce a “Hot” Tumor Microenvironment That Is Responsive to Immunotherapy
Authors: Reschke Robin , Olson Daniel
Published: May 2022

In a T-cell-inflamed phenotype, tumor eradication works best and is potentiated by immunotherapy such as checkpoint blockade. However, a majority of patients die despite receiving immunotherapy. One reason is insufficient T cell priming and infiltration in the tumor. Nature provides us with innate immune mechanisms in T-cell-inflamed tumors that we can adopt for more personalized immunotherapy strategies. Tumor sensing through innate signaling pathways and efficient antigen-presenting possess a significant role in bridging innate and adaptive immunity and generating a T-cell-inflamed tumor. One approach to strengthen these innate immune mechanisms is to deliver innate immune factors such as STING or activated DCs into the tumor microenvironment, in particular in patients resistant to checkpoint blockade. The low number of DCs in the tumor bed could potentially be increased with the growth factor FMS-like tyrosine kinase 3 ligand (Flt3L). CD103+ DCs are integral for three phases of anti-tumor immunity: priming, recruiting, and re-invigoration of effector T cells. Re-activation of dysfunctional T cells is achieved via co-stimulatory molecules such as the 4-1BB ligand. The presence of myeloid-cell-derived CXCL9 and CXCL10 in the tumor microenvironment can predict response to immunotherapy. We outline recent preclinical and clinical approaches to deliver these crucial components bridging innate and adaptive immunity into the tumor microenvironment.
Enhanced Performance of Supported Ternary Metal Catalysts for the Oxidation of Toluene in the Presence of Trichloroethylene
Authors: Dong Tiantian , Liu Kun , Gao Ruyi , Chen Hualian , Yu Xiaohui , Hou Zhiquan , Jing Lin , Deng Jiguang , Liu Yuxi , Dai Hongxing
Published: May 2022

Chlorinated volatile organic compounds (CVOCs), even in small quantities, can cause Pt-based catalyst poisoning. Improving the low-temperature chlorine resistance of catalysts is of vital importance for industrial application, although it remains challenging. Considering actual industrial production, a TiO2-supported ternary metal catalyst was prepared in this work to study the catalytic oxidation of multicomponent VOCs (toluene and trichloroethylene (TCE)). Among all of the samples, PtWRu/TiO2 and PtWCr/TiO2 exhibited the best catalytic performance for toluene oxidation. In the mixed VOC oxidation, the PtWCr/TiO2 sample showed the best catalytic activity for toluene combustion (a toluene conversion of 90% was achieved at 258 °C and a space velocity of 40,000 mL g−1 h−1, and the specific reaction rate and turnover frequency at 215 °C were 44.9 × 10−6 mol gPt−1 s−1 and 26.2 × 10−5 s−1). The PtWRu/TiO2 sample showed the best catalytic activity for TCE combustion (a TCE conversion of 90% was achieved at 305 °C and a space velocity of 40,000 mL g−1 h−1, and the specific reaction rate and turnover frequency at 270 °C were 9.0 × 10−6 mol gPt−1 s−1 and 7.3 × 10–5 s−1). We concluded that the ternary metal catalysts could greatly improve chlorine desorption by increasing the active lattice oxygen mobility and surface acidity, thus reducing chlorinated byproducts and other serious environmental pollutants. This work may serve as a reasonable design reference for solving more practical industrial production emissions of multicomponent VOCs.
Direct Construction of K-Fe3C@C Nanohybrids Utilizing Waste Biomass of Pomelo Peel as High-Performance Fischer–Tropsch Catalysts
Authors: Qiu Songbai , Chen Jianfeng , Fan Yujian , Huang Zan , Meng Qingwei , Ma Liang , Zhang Qian , Wang Tiejun
Published: May 2022

As the only renewable organic carbon source, abundant biomass has long been established and developed to mass-produce functionalized carbon materials. Herein, an extremely facile and green strategy was executed for the first time to in situ construct K-Fe3C@C nanohybrids directly by one-pot carbonizing the pomelo peel impregnated with Fe(NO3)3 solutions. The pyrolytically self-assembled nanohybrids were successfully applied in Fischer–Tropsch synthesis (FTS) and demonstrated high catalytic performance. Accordingly, the optimized K-Fe3C@C catalysts revealed excellent FTS activity (92.6% CO conversion) with highlighted C5+ hydrocarbon selectivity of 61.3% and light olefin (C2-4=) selectivity of 26.0% (olefin/paraffin (O/P) ratio of 6.2). Characterization results further manifest that the high performance was correlated with the in situ formation of the core-shell nanostructure consisting of Fe3C nanoparticles enwrapped by graphitized carbon shells and the intrinsic potassium promoter originated in pomelo peel during high-temperature carbonization. This work provided a facile approach for the low-cost mass-fabrication of high-performance FTS catalysts directly utilizing waste biomass without any chemical pre-treatment or purification.
Evaluation of the Efficacy of an Elastin-Inducing Composition Containing Amino Acids, Copper, and Hyaluronic Acid: Results of an Open Single-Center Clinical Trial Study
Authors: Kim Man-Seok , Chun Ko-Eun , Lee Dong-Keun , Song Seh-Hyon
Published: May 2022

The degradation and reduction in number of extracellular matrix (ECM) proteins are representative biological changes associated with decreased elasticity resulting in various skin problems. Elastin is an ECM protein that plays an important role in maintaining the skin’s structure. It is highly elastic and helps the tissue regain its shape after stretching or contracting. We aimed to evaluate the efficacy of the product containing amino acids, copper, and hyaluronic acid on the improvement of skin aging. A small open single-center study involved four treatments performed on five subjects at 1-week intervals with Elastic Lab®. As a result, eye wrinkles, skin moisture, inner elasticity, thickness, and density were improved 1 week after the last treatment in all subjects compared to the baseline. Among all evaluation items, skin elasticity, thickness, and density showed significant increases. Therefore, by using a composition containing amino acids, minerals, and hyaluronic acid, the biosynthesis of elastin and collagen in the skin increases, restoring skin elasticity and improving various skin problems.
KERRA, Mixed Medicinal Plant Extracts, Inhibits SARS-CoV-2 Targets Enzymes and Feline Coronavirus
Authors: Seetaha Supaphorn , Khamplong Phatcharin , Wanaragthai Panatda , Aiebchun Thitinan , Ratanabunyong Siriluk , Krobthong Sucheewin , Yingchutrakul Yodying , Rattanasrisomporn Jatuporn , Choowongkomon Kiattawee
Published: May 2022

The COVID-19 pandemic affects all parameters, especially healthcare professionals, drugs and medical supplies. The KERRA is a mixed medicinal plant capsule that is used for the treatment of patients with high fever, with food and drug administration approved by FDA Thailand. Recently, KERRA showed induced quicker recovery for COVID-19 patients. Therefore, it is possible that some ingredients in KERRA could inhibit SARS-CoV-2. In this study, two important replication-related enzymes in SARS-CoV-2, a main protease and an RNA-dependent RNA polymerase (RdRp), were used to study the effect of KERRA. The results showed that KERRA inhibited the SARS-CoV-2 main protease and SARS-CoV-2 RdRp with IC50 values of 49.91 ± 1.75 ng/mL and 36.23 ± 5.23 µg/mL, respectively. KERRA displayed no cytotoxic activity on macrophage cells at concentrations lower than 1 mg/mL and exhibited anti-inflammatory activity. Additionally, KERRA was used against a feline coronavirus (feline infectious peritonitis (FIP)) infection with an EC50 value of 134.3 μg/mL. This study supports the potential use of KERRA as a candidate drug for COVID-19.
The Correlation for Effective Distribution Coefficient with Initial Impurity Concentration and Growth Rate for Acrylic Acid in Melt Crystallization
Authors: Shiau Lie-Ding
Published: May 2022

The layer growth rates and resulting crystal purity during solid-layer melt crystallization were experimentally measured for acrylic acid (AA) with impurity propionic acid (PA) operated at various cooling temperatures. A power law was adopted to correlate the growth rate with the temperature difference between melt and coolant. The effective distribution coefficient was determined from the resulting crystal purity for each condition. An empirical equation modified from the analytical solution for the mass transfer boundary layer was proposed in this work to relate the effective distribution coefficient to the initial impurity concentration and growth rate.
The Role of Microorganisms in Appendiceal Pseudomyxoma Peritonei: A Review
Authors: Khamzina Yekaterina , King Mary Caitlin , Nieroda Carol , Merrell D. Scott , Sardi Armando , Gushchin Vadim
Published: May 2022

Pseudomyxoma peritonei (PMP) is a rare clinical syndrome. It originates from neoplasms of the appendix and leads to the formation of peritoneal implants and the accumulation of mucinous ascites. PMP represents a spectrum of low to high-grade disease. Despite aggressive management, many PMP patients recur, leading to debilitating symptoms and few treatment options. Therefore, scientists have continued to look for ways to improve treatment and further understand disease pathogenesis. Microorganisms were previously hypothesized to play a role in PMP progression and development. Hence, antibacterial treatment was suggested by some authors, but the data were limited. In this paper, we review the current data on the role of bacteria in PMP, discuss the significance, and suggest possible solutions to the inherent challenges in these studies. Given the limitations of the discussed studies, we remain skeptical about introducing novel antibacterial treatment into clinical practice at this time; however, the available data are valuable and indicate that more research into the molecular mechanisms of PMP is needed.
Platelet, Fibrinolytic and Other Coagulation Abnormalities in Newly-Diagnosed Patients with Chronic Thromboembolic Pulmonary Hypertension
Authors: Vrigkou Eleni , Tsantes Argirios , Konstantonis Dimitrios , Rapti Evdoxia , Maratou Eirini , Pappas Athanasios , Halvatsiotis Panagiotis , Tsangaris Iraklis
Published: May 2022

The pathophysiological background of chronic thromboembolic pulmonary hypertension (CTEPH) has not been fully elucidated. Evidence suggests that abnormal platelet function and ineffective fibrinolysis may play a key role in the development of the disease. The purpose of this study was to evaluate platelet and coagulation function in CTEPH, using non-conventional global coagulation assays, and platelet activation and endothelial dysfunction laboratory markers. A total of 40 newly-diagnosed CTEPH patients were studied, along with 35 healthy controls. Blood samples from CTEPH patients were taken directly from the pulmonary artery. All subjects were assessed with platelet function analyzer-100, light transmission aggregometry, thromboelastometry, endogenous thrombin potential. von Willebrand antigen and activity, p-selectin, thromboxane A2 and serotonin levels were also assessed. The results showed that CTEPH patients present diminished platelet aggregation, presence of disaggregation, decreased rate of fibrinolysis, defective thrombin generation and increased levels of thromboxane A2, p-selectin, von Willebrand antigen and activity. Serotonin levels did not present any differences between the two groups. The results of this study suggest that CTEPH patients present platelet function, fibrinolytic, thrombin generation and other clot formation abnormalities. Well-designed clinical studies are needed to further evaluate the complex hemostatic abnormalities in the CTEPH setting and assess their potential clinical applications.
Replication in Energy Markets: Use and Misuse of Chaos Tools
Authors: Mastroeni Loretta , Vellucci Pierluigi
Published: May 2022

As pointed out by many researchers, replication plays a key role in the credibility of applied sciences and the confidence in all research findings. With regard, in particular, to energy finance and economics, replication papers are rare, probably because they are hampered by inaccessible data, but their aim is crucial. We consider two ways to avoid misleading results on the ostensible chaoticity of price series. The first one is represented by the proper mathematical definition of chaos and the related theoretical background, while the latter is represented by the hybrid approach that we propose here—i.e., consisting of considering the dynamical system underlying the price time series as a deterministic system with noise. We find that both chaotic and stochastic features coexist in the energy commodity markets, although the misuse of some tests in the established practice in the literature may say otherwise.
RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network
Authors: Yang Xiaoteng , Wu Zhenqiang , Javaid Shumaila
Published: May 2022

The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution.
Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks
Authors: Hajarathaiah Koduru , Enduri Murali Krishna , Anamalamudi Satish , Subba Reddy Tatireddy , Tokala Srilatha
Published: May 2022

Computing influential nodes gets a lot of attention from many researchers for information spreading in complex networks. It has vast applications, such as viral marketing, social leader creation, rumor control, and opinion monitoring. The information-spreading ability of influential nodes is greater compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in a complex network, such as degree, betweenness, closeness, semi-local centralities, and PageRank. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to their high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the nearest neighborhood trust PageRank (NTPR) based on the structural attributes of neighbors and nearest neighbors of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust values of neighbors, and the nearest neighbors. We computed the influential nodes in various real-world networks using the proposed centrality method. We found the maximum influence by using influential nodes with SIR and independent cascade methods. We also compare the maximum influence of our centrality measure with the existing basic centrality measures.
Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition
Authors: Yang Haihui , Huang Shiguo , Guo Shengwei , Sun Guobing
Published: May 2022

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.
Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
Authors: Wang Tiancheng , Usuda Tsuyoshi Sasaki
Published: May 2022

We propose an amplitude shift keying-type asymmetric quantum communication (AQC) system that uses an entangled state. As a first step toward development of this system, we evaluated and considered the communication performance of the proposed receiver when applied to the AQC system using a two-mode squeezed vacuum state (TSVS), the maximum quasi-Bell state, and the non-maximum quasi-Bell state, along with an asymmetric classical communication (ACC) system using the coherent state. Specifically, we derived an analytical expression for the error probability of the AQC system using the quasi-Bell state. Comparison of the error probabilities of the ACC system and the AQC systems when using the TSVS and the quasi-Bell state shows that the AQC system using the quasi-Bell state offers a clear performance advantage under specific conditions. Additionally, it was clarified that there are cases where the universal lower bound on the error probability for the AQC system was almost achieved when using the quasi-Bell state, unlike the case in which the TSVS was used.
Jaynes-Gibbs Entropic Convex Duals and Orthogonal Polynomials
Authors: Le Blanc Richard
Published: May 2022

The univariate noncentral distributions can be derived by multiplying their central distributions with translation factors. When constructed in terms of translated uniform distributions on unit radius hyperspheres, these translation factors become generating functions for classical families of orthogonal polynomials. The ultraspherical noncentral t, normal N, F, and χ2 distributions are thus found to be associated with the Gegenbauer, Hermite, Jacobi, and Laguerre polynomial families, respectively, with the corresponding central distributions standing for the polynomial family-defining weights. Obtained through an unconstrained minimization of the Gibbs potential, Jaynes’ maximal entropy priors are formally expressed in terms of the empirical densities’ entropic convex duals. Expanding these duals on orthogonal polynomial bases allows for the expedient determination of the Jaynes–Gibbs priors. Invoking the moment problem and the duality principle, modelization can be reduced to the direct determination of the prior moments in parametric space in terms of the Bayes factor’s orthogonal polynomial expansion coefficients in random variable space. Genomics and geophysics examples are provided.
Biology, Buddhism, and AI: Care as the Driver of Intelligence
Authors: Doctor Thomas , Witkowski Olaf , Solomonova Elizaveta , Duane Bill , Levin Michael
Published: May 2022

Intelligence is a central feature of human beings’ primary and interpersonal experience. Understanding how intelligence originated and scaled during evolution is a key challenge for modern biology. Some of the most important approaches to understanding intelligence are the ongoing efforts to build new intelligences in computer science (AI) and bioengineering. However, progress has been stymied by a lack of multidisciplinary consensus on what is central about intelligence regardless of the details of its material composition or origin (evolved vs. engineered). We show that Buddhist concepts offer a unique perspective and facilitate a consilience of biology, cognitive science, and computer science toward understanding intelligence in truly diverse embodiments. In coming decades, chimeric and bioengineering technologies will produce a wide variety of novel beings that look nothing like familiar natural life forms; how shall we gauge their moral responsibility and our own moral obligations toward them, without the familiar touchstones of standard evolved forms as comparison? Such decisions cannot be based on what the agent is made of or how much design vs. natural evolution was involved in their origin. We propose that the scope of our potential relationship with, and so also our moral duty toward, any being can be considered in the light of Care—a robust, practical, and dynamic lynchpin that formalizes the concepts of goal-directedness, stress, and the scaling of intelligence; it provides a rubric that, unlike other current concepts, is likely to not only survive but thrive in the coming advances of AI and bioengineering. We review relevant concepts in basal cognition and Buddhist thought, focusing on the size of an agent’s goal space (its cognitive light cone) as an invariant that tightly links intelligence and compassion. Implications range across interpersonal psychology, regenerative medicine, and machine learning. The Bodhisattva’s vow (“for the sake of all sentient life, I shall achieve awakening”) is a practical design principle for advancing intelligence in our novel creations and in ourselves.
Predicting Box-Office Markets with Machine Learning Methods
Authors: Li Dawei , Liu Zhi-Ping
Published: May 2022

The accurate prediction of gross box-office markets is of great benefit for investment and management in the movie industry. In this work, we propose a machine learning-based method for predicting the movie box-office revenue of a country based on the empirical comparisons of eight methods with diverse combinations of economic factors. Specifically, we achieved a prediction performance of the relative root mean squared error of 0.056 in the US and of 0.183 in China for the two case studies of movie markets in time-series forecasting experiments from 2013 to 2016. We concluded that the support-vector-machine-based method using gross domestic product reached the best prediction performance and satisfies the easily available information of economic factors. The computational experiments and comparison studies provided evidence for the effectiveness and advantages of our proposed prediction strategy. In the validation process of the predicted total box-office markets in 2017, the error rates were 0.044 in the US and 0.066 in China. In the consecutive predictions of nationwide box-office markets in 2018 and 2019, the mean relative absolute percentage errors achieved were 0.041 and 0.035 in the US and China, respectively. The precise predictions, both in the training and validation data, demonstrate the efficiency and versatility of our proposed method.
Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems
Authors: Sason Igal
Published: May 2022

Data science, information theory, probability theory, statistical learning, statistical signal processing, and other related disciplines greatly benefit from non-negative measures of dissimilarity between pairs of probability measures [...]
Focused Information Criterion for Restricted Mean Survival Times: Non-Parametric or Parametric Estimators
Authors: Nemes Szilárd , Gustavsson Andreas , Jauhiainen Alexandra
Published: May 2022

Restricted Mean Survival Time (RMST), the average time without an event of interest until a specific time point, is a model-free, easy to interpret statistic. The heavy reliance on non-parametric or semi-parametric methods in the survival analysis has drawn criticism, due to the loss of efficacy compared to parametric methods. This assumes that the parametric family used is the true one, otherwise the gain in efficacy might be lost to interpretability problems due to bias. The Focused Information Criterion (FIC) considers the trade-off between bias and variance and offers an objective framework for the selection of the optimal non-parametric or parametric estimator for scalar statistics. Herein, we present the FIC framework for the selection of the RMST estimator with the best bias-variance trade-off. The aim is not to identify the true underling distribution that generated the data, but to identify families of distributions that best approximate this process. Through simulation studies and theoretical reasoning, we highlight the effect of censoring on the performance of FIC. Applicability is illustrated with a real life example. Censoring has a non-linear effect on FICs performance that can be traced back to the asymptotic relative efficiency of the estimators. FICs performance is sample size dependent; however, with censoring percentages common in practical applications FIC selects the true model at a nominal probability (0.843) even with small or moderate sample sizes.