bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation and Optimization of Prolonged Release Mucoadhesive Tablets of Dexamethasone for Wound Healing: In Vitro–In Vivo Profiling in Healthy Volunteers
Authors: Javed Qurrat ul Ain , Syed Muhammad Ali , Arshad Rabia , Rahdar Abbas , Irfan Muhammad , Raza Syed Atif , Shahnaz Gul , Hanif Sana , Díez-Pascual Ana M.
Published: April 2022

The aim of the projected study was to design and develop a novel strategy for evaluating the mucoadhesive potential of polymeric tablets of dexamethasone (DXM) for local delivery against wounds. Therefore, formulations (Q1–Q7) were synthesized via direct compression method by varying the concentrations of polymers, i.e., ethyl cellulose (EC) and agar extract (AG). Moreover, the mucoadhesive polymeric tablets were characterized via physicochemical, in vitro, ex vivo and in vivo experiments. However, physicochemical characteristics such as FTIR showed no interaction with different polymeric combination. Surface pH of all formulations was normal to slightly alkaline. Highest hydration of up to 6.22% and swelling index was comprehended with maximum concentration of AG (50% of total tablet weight). Whereas, ex vivo and in vivo residence time and mucoadhesion were attributed to the increased concentrations of polymers. Moreover, Q7, (optimized formulation), containing 10% of EC and 40% of AG, exhibited maximum release of DXM (100%) over 8 h, along with sufficient mucoadhesive strength up to 11.73 g, following first-order kinetics having r2 value of 0.9778. Hemostatic effects and epithelialization for triggering and promoting wound healing were highly pronounced in cases of Q7. Furthermore, in vivo residence time was 7.84 h followed by salivary drug concentration (4.2 µg/mL). However, mucoadhesive buccal tablets showed stability for 6 months, thus following the standardization (ICH-Iva) stability zone. In summary, DXM mucoadhesive tablets seem to be an ideal candidate for eradication of wound infections via local targeted delivery.
Highway to Cell: Selection of the Best Cell-Penetrating Peptide to Internalize the CFTR-Stabilizing iCAL36 Peptide
Authors: Seisel Quentin , Lakumpa Israpong , Josse Emilie , Vivès Eric , Varilh Jessica , Taulan-Cadars Magali , Boisguérin Prisca
Published: April 2022

Therapeutic peptides have regained interest as they can address unmet medical needs and can be an excellent complement to pharmaceutic small molecules and other macromolecular therapeutics. Over the past decades, correctors and potentiators of the cystic fibrosis transmembrane conductance regulator (CFTR), a chloride ion channel causing cystic fibrosis (CF) when mutated, were developed to reduce the symptoms of the patients. In this context, we have previously designed a CFTR-stabilizing iCAL36 peptide able to further increase the CFTR amount in epithelial cells, thereby resulting in a higher CFTR activity. In the present study, optimization of the peptidyl inhibitor was performed by coupling five different cell-penetrating peptides (CPP), which are Tat, dTat, TatRI (retro-inverso), MPG, and Penetratin. Screening of the internalization properties of these CPP-iCAL36 peptides under different conditions (with or without serum or endocytosis inhibitors, etc.) was performed to select TatRI as the optimal CPP for iCAL36 delivery. More importantly, using this TatRI-iCAL36 peptide, we were able to reveal for the first time an additive increase in the CFTR amount in the presence of VX-445/VX-809 compared to VX-445/VX-809 treatment alone. This finding is a significant contribution to the development of CFTR-stabilizing peptides in addition to currently used treatments (small-molecule correctors or potentiators) for CF patients.
Delivery Systems of Plasmid DNA and Messenger RNA for Advanced Therapies
Authors: Uchida Satoshi
Published: April 2022

The vast potential of non-viral delivery systems of messenger RNA (mRNA) and plasmid DNA (pDNA) has been demonstrated in the vaccines against coronavirus disease 2019 (COVID-19) [...]
Population Pharmacokinetics of Valproic Acid in Pediatric and Adult Caucasian Patients
Authors: Teixeira-da-Silva Paulo , Pérez-Blanco Jonás Samuel , Santos-Buelga Dolores , Otero María José , García María José
Published: April 2022

(1) Background: The aim of this study was to explore the valproic acid (VPA) pharmacokinetic characteristics in a large population of pediatric and adult Caucasian patients and to establish a robust population pharmacokinetic (PopPK) model. (2) Methods: A total of 2527 serum VPA samples collected from 1204 patients included in a therapeutic drug monitoring program were retrospectively analyzed. Patients were randomly assigned to either a model development group or an external evaluation group. PopPK analysis was performed on 1751 samples from 776 patients with NONMEM using a nonlinear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment and comedication variables on the apparent clearance (CL/F) of VPA was studied. The bootstrap method was used to evaluate the final model internally. External evaluation was carried out using 776 VPA serum samples from 368 patients. (3) Results: A one-compartment model with first-order absorption and elimination successfully described the data. The final model included total body weight, age and comedication with phenytoin, phenobarbital and carbamazepine with a significant impact on VPA elimination. Internal and external evaluations demonstrated the good predictability of the model. (4) Conclusions: A PopPK model of VPA in Caucasian patients was successfully established, which will be helpful for model-informed precision dosing approaches in clinical patient care.
A New Generation of Ultrasmall Nanoparticles Inducing Sensitization to Irradiation and Copper Depletion to Overcome Radioresistant and Invasive Cancers
Authors: Rocchi Paul , Brichart-Vernos Delphine , Lux François , Morfin Isabelle , David Laurent , Rodriguez-Lafrasse Claire , Tillement Olivier
Published: April 2022

An emerging target to overcome cancer resistance to treatments is copper, which is upregulated in a wide variety of tumors and may be associated with cancer progression and metastases. The aim of this study was to develop a multimodal ultrasmall nanoparticle, CuPRiX, based on the clinical AGuIX nanoparticle made of the polysiloxane matrix on which gadolinium chelates are grafted. Such hybrid nanoparticles allow: (i) a localized depletion of copper in tumors to prevent tumor cell dissemination and metastasis formation and (ii) an increased sensitivity of the tumor to radiotherapy (RT) due to the presence of high Z gadolinium (Gd) atoms. CuPRiX nanoparticles are obtained by controlled acidification of AGuIX nanoparticles. They were evaluated in vitro on two cancer cell lines (lung and head and neck) using the scratch-wound assay and clonogenic cell survival assay. They were able to reduce cell migration and invasion and displayed radiosensitizing properties.
Rising Carbon Dioxide and Global Nutrition: Evidence and Action Needed
Authors: Ziska Lewis
Published: April 2022

While the role of CO2 as a greenhouse gas in the context of global warming is widely acknowledged, additional data from multiple sources is demonstrating that rising CO2 of and by itself will have a tremendous effect on plant biology. This effect is widely recognized for its role in stimulating photosynthesis and growth for multiple plant species, including crops. However, CO2 is also likely to alter plant chemistry in ways that will denigrate plant nutrition. That role is also of tremendous importance, not only from a human health viewpoint, but also from a global food–web perspective. Here, the goal is to review the current evidence, propose potential mechanistic explanations, provide an overview of critical unknowns and to elucidate a series of next steps that can address what is, overall, a critical but unappreciated aspect of anthropogenic climate change.
RNA-Seq of Cyst Nematode Infestation of Potato (Solanum tuberosum L.): A Comparative Transcriptome Analysis of Resistant and Susceptible Cultivars
Authors: Chandrasekar Saranya , Natarajan Purushothaman , Mhatre Priyank Hanuman , Mahajan Mahesh , Nivitha Sundararaj , Palanisamy Venkatasalam E. , Reddy Umesh K. , Sundararaj Palanisamy
Published: April 2022

Potato (Solanum tuberosum L.) is an important food crop worldwide, and potato cyst nematodes (PCNs) are among the most serious pests. The identification of disease resistance genes and molecular markers for PCN infestation can aid in crop improvement research programs against PCN infestation. In the present study, we used high-throughput RNA sequencing to investigate the comprehensive resistance mechanisms induced by PCN infestation in the resistant cultivar Kufri Swarna and the susceptible cultivar Kufri Jyoti. PCN infestation induced 791 differentially expressed genes in resistant cultivar Kufri Swarna, comprising 438 upregulated and 353 downregulated genes. In susceptible cultivar Kufri Jyoti, 2225 differentially expressed genes were induced, comprising 1247 upregulated and 978 downregulated genes. We identified several disease resistance genes (KIN) and transcription factors (WRKY, HMG, and MYB) that were upregulated in resistant Kufri Swarna. The differentially expressed genes from several enriched KEGG pathways, including MAPK signaling, contributed to the disease resistance in Kufri Swarna. Functional network analysis showed that several cell wall biogenesis genes were induced in Kufri Swarna in response to infestation. This is the first study to identify underlying resistance mechanisms against PCN and host interaction in Indian potato varieties.
Patterns of Plumericin Concentration in Leaves of Himatanthus tarapotensis (Apocynaceae) and Its Interactions with Herbivory in the Peruvian Amazon
Authors: Amasifuen Guerra Carlos A. , Patel Kirti , Delprete Piero G. , Spina Andréa P. , Grados Juan , Vásquez-Ocmín Pedro , Gadea Alice , Rojas Rosario , Guzmán Jesús , Sauvain Michel
Published: April 2022

We explored the concentration patterns of the bioactive metabolite plumericin produced by Himatanthus tarapotensis (Apocynaceae) under different edaphic conditions and variations in rainfall intensity, as well as its potential role in the chemical defense against insect herbivores. Values of plumericin concentration from leaves were obtained by High-Performance Liquid Chromatography, and evaluated as a function of differences in soil types, variation of precipitation, and variation of the abundance of insect herbivores, using first a Repeated Measures Correlation (rmcorr) and then a Generalized Linear Mixed Model (GLMM) analysis. Plumericin concentration is highly variable among plants, but with a significantly higher concentration in plants growing on clay soil compared to that of the white-sand soil habitat (p < 0.001). Plumericin concentration is not affected by precipitation. The caterpillar of Isognathus leachii (Lepidoptera: Sphingidae) is the most conspicuous herbivore of H. tarapotensis, and its presence is continuous but not related to plumericin concentration, probably because of its capacity to elude the chemical defense of this plant. Nevertheless, our multivariate model revealed that plumericin concentration is related to the abundance of Hymenoptera (Formicidae), and this relationship is significantly influenced by the soil parameters of carbon percentage, clay percentage, and phosphorous percentage (p < 0.001). Plumericin is a mediating agent in the interaction between H. tarapotensis and its natural environment. Variation in plumericin concentration would be induced by the abundance of Hymenoptera (Formicidae), probably as a chemical response against these insects, and by differences in soil nutrient availability.
Formulation and Characterization of Carbopol-934 Based Kojic Acid-Loaded Smart Nanocrystals: A Solubility Enhancement Approach
Authors: Khan Barkat Ali , Waheed Maryam , Hosny Khaled M. , Rizg Waleed Y. , Murshid Samar S. , Alharbi Majed , Khan Muhammad Khalid
Published: April 2022

Kojic acid (KA) is a BCS class II drug having low solubility and high permeability. This study was designed to enhance the aqueous solubility of KA, as well as its dissolution rate and, in turn, bioavailability, by formulating its smart nanocrystals. Nanocrystals of pure KA were formulated by the top-down method under high-pressure homogenization followed by freeze drying. The nanocrystals were evaluated for stability and other physical characteristics, including zeta sizer analysis, DSC, surface morphology, XRD, drug content, solubility, FTIR and in vitro drug release. The KA nanocrystals were found to be stable when kept at exaggerated conditions. The particle size of the nanocrystals was 137.5 ± 1.7, 150 ± 2.8, and 110 ± 3.0 nm for the F1, F2 and F3 formulations, respectively. There was negative zeta potential for all the formulations. The dispersity index was 0.45 ± 0.2, 0.36 ± 0.4 and 0.41 ± 1.5 for the F1, F2 and F3, respectively. The DSC studies showed that there was no interaction between the KA and the excipients of the nanocrystals. The morphological studies confirmed the presence of rough crystalline surfaces on the nanosized particles. XRD studies showed the successful preparation of nanocrystals. The drug content was in the official range of 90 ± 10%. The solubility of KA was significantly (p < 0.05) enhanced in the formulations of its nanocrystals as compared with pure KA powder. The ATR-FTIR studies revealed the presence of functional groups in both KA and KA-loaded nanocrystals, and no interaction was found between them. The nanocrystals released 83.93 ± 1.22% of KA in 24 h. The study concluded that the nanocrystals were successfully formulated using the top-down method followed by high-pressure homogenization. The solubility, as well as the dissolution, of the KA was enhanced, and this could improve the therapeutic effects of KA.
Using Linkography and Situated FBS Co-Design Model to Explore User Participatory Conceptual Design Process
Authors: Cao Juan , Zhao Wu , Hu Huicong , Liu Yeqi , Guo Xin
Published: April 2022

To unravel the complex challenges addressed by product innovation, it is oftentimes essential for users to participate in the design process. However, there is a paucity of research in terms of in-depth exploration of the cognitive patterns and dynamic design processes of co-design with user participation in the existing design cognition research. The current study aimed to investigate the cognition activities involved in the process of co-design between user and designer at both the individual and team levels. The combination method of linkography and the situated function–behavior–structure (FBS) co-design model was carried out to encode and analyze the protocol data. The results showed that, at the individual level, designers and users adopted different design strategies to promote the progress of the design. In addition, the interaction activities among users and designers varied in different co-design processes. However, at the team level, the collaborators showed systematic thinking modes, and each design move was two-way. This cognitive strategy of the innovation team ensured the continuity and effectiveness of the co-design process. Theoretically, these findings will bring new insights for studies on team cognition activities and contribute to building user-centric design theory by uncovering the dynamic design processes of co-design with user participatory. In addition, the study makes a methodological contribution by illustrating how linkography and the situated FBS co-design model can be utilized to analyze the team cognition during co-design activities.
On the Differences between Han Rhapsodies and Han Paintings in Their Portrayal of the Queen Mother of the West and Their Religious Significance
Authors: Wang Xiaoyang , Wang Shixiao
Published: April 2022

This paper argues that there exist two Queen Mothers of the West (Xiwangmu) in the Han era (206 BC–AD 220): one worshipped as a goddess of longevity and immortality by people from the upper class; the other worshipped by the ordinary people as a seemingly omnipotent deity with divine power over both the immortal world and the mortal world. This argument is based on a thorough comparative investigation of the surviving corpus of Han rhapsodies (fu) and Han paintings, the two major genres of art that give form to her cult in the Han period.
Confucianism for Kids: Early Childhood Employments of Confucianism in Taipei and Tokyo
Authors: Paramore Kiri
Published: April 2022

This article focuses on two examples of Confucian early childhood education in contemporary Taiwan and Japan. Based on fieldwork conducted by the author in 2015, it contrasts the use of Confucianism in a grass-roots community early childhood educational setting in suburban Taipei with attempts to create elite Confucian “kids’ seminars” in central Tokyo. The study reveals the roles of gender, elitism, religious plurality, and modern early childhood pedagogy in the contrasting ways Confucianism manifests in these urban Taiwanese and Japanese settings. In doing so, it looks to contribute to wider discussions about the roles of modernity and tradition in contemporary religious revival in East Asia.
Charismatic, Synchronous and Psychedelic Religious Experiences: A Personal Account
Authors: Macallan Brian
Published: April 2022

Religious experiences and their truth, nature, and influence remain controversial. This is despite their wide cultural expression and significant grounding in research. Personal experiences that are deemed “religious” are often critiqued for being culturally influenced and psychologically misleading, implying that this ought to discredit them. This paper seeks not to argue for the truth or falsity of these kinds of experiences, but rather to offer an autoethnographic response to how these experiences have functioned in this researcher’s life. The response is contextualized broadly within the study of religious experience and contributes towards that field. Three forms of religious and spiritual experiences are offered narratively, from my early Charismatic experience, to synchronous encounters, as well as the exploration of psilocybin. It is suggested that these experiences are broadly shared, and that they can be personally as well as socially transformative. Although they are autoethnographic in nature, all three forms are situated within the context from which they emerged.
Water-Body Segmentation for SAR Images: Past, Current, and Future
Authors: Guo Zhishun , Wu Lin , Huang Yabo , Guo Zhengwei , Zhao Jianhui , Li Ning
Published: April 2022

Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation.
Agents of Forest Disturbance in the Argentine Dry Chaco
Authors: De Marzo Teresa , Gasparri Nestor Ignacio , Lambin Eric F. , Kuemmerle Tobias
Published: April 2022

Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km2 Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km2 (±407 km2), followed by fire (4562 ± 388 km2) and logging (3891 ± 341 km2). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed.
A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series
Authors: Lin Xiuchun , Wa Shiyun , Zhang Yan , Ma Qin
Published: April 2022

Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers’ livelihoods and society’s flourishment. Meanwhile, detecting crops in farming areas, such as wheat and corn, allows for more direct monitoring of farming area production and is significant for practical production and management. However, existing image segmentation methods are relatively homogeneous, with insufficient ability to segment multiple objects around the agricultural environment and small-scale objects such as corn and wheat. Motivated by these issues, this paper proposed a global-transformer segmentation network based on the morphological correction method. In addition, we applied the dilated convolution technique to the backbone of the model and the transformer technique to the branches. This innovation of integrating the above-mentioned techniques has an active impact on the segmentation of small-scale objects. Subsequently, the backbone improved by this method was applied to an object detection network based on a corn and wheat ears dataset. Experimental results reveal that our model can effectively detect wheat ears in a complicated environment. For two particular segmentation objects in farming areas, namely water bodies and roads, we notably proposed a morphological correction method, which effectively reduces the number of connected domains in the segmentation results with different parameters of dilation and erosion operations. The segmentation results of water bodies and roads were thereby improved. The proposed method achieved 0.903 and 13 for mIoU and continuity. This result reveals a remarkable improvement compared with the comparison model, and the continuity has risen by 408%. These comparative results demonstrate that the proposed method is eminent and robust enough to provide preliminary preparations and viable strategies for managing farming area resources and detecting crops.
Characterising the Aboveground Carbon Content of Saltmarsh in Jervis Bay, NSW, Using ArborCam and PlanetScope
Authors: Warwick-Champion Elizabeth , Davies Kevin P. , Barber Paul , Hardy Naviin , Bruce Eleanor
Published: April 2022

Coastal ecosystems, including saltmarsh, provide important ecosystem services, including blue carbon storage, nutrient cycling, and coastal protection. The loss or degradation of saltmarsh ecosystems may undermine their capacity to provide these services and drive carbon emission increases. The accurate mapping and monitoring of the aboveground carbon content in these ecosystems supports protection and rehabilitation activities. Previous studies have used medium resolution satellites (e.g., Landsat and Sentinel-2) to characterise saltmarsh communities; however, these platforms are not well suited to the fine-scale patchiness of the saltmarsh ecosystems found in Australia. Here we explore the potential of a very high spatial resolution (0.15 m), seven-band multispectral ArborCam airborne sensor and 3 m images captured by the PlanetScope satellite constellation for mapping and monitoring the aboveground carbon content of a saltmarsh ecosystem in Jervis Bay National Park, Australia. The Normalized Difference Vegetation Index (NDVI) derived from an ArborCam image was calibrated to aboveground carbon content using field survey data. Strong linear relationships between the ArborCam NDVI and aboveground carbon content were found when survey data were partitioned by species. The mean aboveground carbon content derived from the calibrated ArborCam image was 1.32 Mg C ha−1 across the study area; however, this is likely to have been underestimated. A monthly NDVI time series derived from 12 PlanetScope images was analysed to investigate the short-term temporal variation in saltmarsh phenology, and significant intra-annual variation was found. An exploration of potential drivers for the variation found that local rainfall was a potential driver. The combination of the very high spatial resolution airborne ArborCam image and the regular 3 m capture by PlanetScope satellites was found to have potential for accurate mapping and monitoring of aboveground carbon in saltmarsh communities. Future work will focus on improving aboveground carbon estimates by including a very high spatial resolution species distribution map and investigating the influence of temporal variations in saltmarsh spectral response on these estimates.
Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data
Authors: Bonsoms Josep , Boulet Gilles
Published: April 2022

Estimating evapotranspiration at the field scale is a major component of sustainable water management. Due to the difficulty to assess some major unknowns of the water cycle at that scale, including irrigation amounts, evapotranspiration is often computed as the residual of the instantaneous surface energy budget. One of the Surface Energy Balance components with the largest uncertainties in their quantification over bare soils and sparse vegetation areas is the ground heat flux (G). Over the last decades, the estimation of G with remote sensing (RS) data has been mainly achieved with empirical equations, on the basis of the G and net radiation (Rn) ratio, G/Rn. The G/Rn empirical equations generally require vegetation data (Type I empirical equations), in combination with surface temperature (Ts) and albedo (Type II empirical equations). In this article, we aim to evaluate the estimation of G with RS data. Here, we compared eight G/Rn empirical equations against two types of machine learning (ML) methods: an ensemble ML type, the Random Forest (RF), and the Neural Networks (NN). The comparison of each method was evaluated using a wide range of climate and land cover datasets, including data from Eddy-Covariance towers that extend along the mid-latitude areas that encompass the European and African continents. Our results have shown evidence that the driver of G in bare soils and sparse vegetation areas (Fraction of Vegetation, Fv ≤ 0.25) is Ts, instead of vegetation greenness indexes. On the other hand, the accuracy in the estimation of G with Rn, Ts or Fv decreases in densely vegetated areas (Fv ≥ 0.50). There are no significant differences between the most accurate Type I and II empirical equations. For bare soils and sparse vegetation areas the empirical equation which combines the Leaf Area Index (LAI) and Ts (E7) estimates G best. In densely vegetated areas, an exponential empirical equation based on Fv (E4), shows the best performance. However, ML better estimates G than the empirical equations, independently of the Fv ranges. An RF model with Rn, LAI and Ts as predictor variables shows the best accuracy and performance metrics, outperforming the NN model.
An Information Spatial-Temporal Extension Algorithm for Shipborne Predictions Based on Deep Neural Networks with Remote Sensing Observations—Part I: Ocean Temperature
Authors: Mao Kai , Gao Feng , Zhang Shaoqing , Liu Chang
Published: April 2022

For ships on voyage, using satellite remote sensing observations is an effective way to access ocean temperature. However, satellite remote sensing observations can only provide the surface information. Additionally, this information obtained from satellite remote sensing observations is delayed data. Although some previous studies have investigated the spatial inversion (spatial extension) or temporal prediction (temporal extension) of satellite remote sensing observations, these studies did not integrate ship survey observations and the temporal prediction is limited to sea surface temperature (SST). To address these issues, we propose an information spatial-temporal extension (ISTE) algorithm for remote sensing SST. Based on deep neural networks (DNNs), the ISTE algorithm can effectively fuse the satellite remote sensing SST data, ship survey observations data, and historical data to generate a four-dimensional (4D) temperature prediction field. Experimental results show that the ISTE algorithm performs superior prediction accuracy relative to linear regression analysis-based prediction. The prediction results of ISTE exhibit high coefficient of determination (0.9936) and low root mean squared errors (around 0.7 °C) compared with Argo observation data. Therefore, for shipborne predictions, the ISTE algorithm driven by satellite remote sensing SST can be as an effective approach to predict ocean temperature.
Apoptosis and Pharmacological Therapies for Targeting Thereof for Cancer Therapeutics
Authors: Singh Vishakha , Khurana Amit , Navik Umashanker , Allawadhi Prince , Bharani Kala Kumar , Weiskirchen Ralf
Published: April 2022

Apoptosis is an evolutionarily conserved sequential process of cell death to maintain a homeostatic balance between cell formation and cell death. It is a vital process for normal eukaryotic development as it contributes to the renewal of cells and tissues. Further, it plays a crucial role in the elimination of unnecessary cells through phagocytosis and prevents undesirable immune responses. Apoptosis is regulated by a complex signaling mechanism, which is driven by interactions among several protein families such as caspases, inhibitors of apoptosis proteins, B-cell lymphoma 2 (BCL-2) family proteins, and several other proteases such as perforins and granzyme. The signaling pathway consists of both pro-apoptotic and pro-survival members, which stabilize the selection of cellular survival or death. However, any aberration in this pathway can lead to abnormal cell proliferation, ultimately leading to the development of cancer, autoimmune disorders, etc. This review aims to elaborate on apoptotic signaling pathways and mechanisms, interacting members involved in signaling, and how apoptosis is associated with carcinogenesis, along with insights into targeting apoptosis for disease resolution.
Constructing Features Using a Hybrid Genetic Algorithm
Authors: Tsoulos Ioannis G.
Published: April 2022

A hybrid procedure that incorporates grammatical evolution and a weight decaying technique is proposed here for various classification and regression problems. The proposed method has two main phases: the creation of features and the evaluation of these features. During the first phase, using grammatical evolution, new features are created as non-linear combinations of the original features of the datasets. In the second phase, based on the characteristics of the first phase, the original dataset is modified and a neural network trained with a genetic algorithm is applied to this dataset. The proposed method was applied to an extremely wide set of datasets from the relevant literature and the experimental results were compared with four other techniques.
How Does Race Moderate the Effect of Religion Dimensions on Attitudes toward the Death Penalty?
Authors: Sabriseilabi Soheil , Williams James , Sadri Mahmoud
Published: April 2022

We examined the moderating role of race on the relationship between religion and death penalty attitudes in the United States. We operationalized religion by distinguishing four dimensions: religiosity, spirituality, afterlife beliefs, and denomination. Using 2018 General Social Survey data from 1054 adults, collected by the National Opinion Research Center at the University of Chicago, we show that the impact of each dimension of religion varies across racial groups. Logistic Regression results showed that the likelihood of support for the death penalty was associated with religiosity, spirituality, belief in hell, being female, and being liberal. Adding race as an interaction term moderated the associations of religiosity and spirituality.
“I Can See Myself in Them, but They Are Not Me”: Asian Boys’ Love (BL) Drama and Gay Male Viewers
Authors: Jerome Collin , Ahmad Hadzmy Ahmad Junaidi bin , Su Hie Ting
Published: April 2022

Recent years have witnessed significant growth in research on the phenomenon of Asian Boys’ Love (BL) dramas, a new entertainment genre that features male characters who engage in same-sex love relations. However, much of this research has focused on, among others, heterosexual female viewers in terms of their knowledge of BL and their consumption patterns and motives. The present study aims at addressing this knowledge gap by gathering the views of gay male viewers regarding the BL that holds a place in their lives. In-depth interviews were conducted with six gay-identified participants living in Malaysia, with the study informed and guided by the framework of audience uses and appropriations of BL, and with the data collected and analyzed following the interpretative phenomenological approach (IPA) procedures. The findings revealed, on the one hand, polarized consumption patterns and, on the other hand, homogeneous motives, and reasons for consuming BL, including pure escapism, self-identification, and pure entertainment. The findings also revealed the participants’ views on whether the BL drama represented their identity and the real-life struggles of being a gay man in a country where homosexuality is still condemned and made illegal. These findings have implications that are relevant to the discussion of the role of the homoerotic media as a source of identity and livelihood among gay men.
Being an Inclusive Researcher in a National Consultation Exercise—A Case Study
Authors: Westermann Greta , Whittle Erin Louise , Adrian Susan , Jessep Suzanne , Nolan Melanie , O’Brien Bruce , Pannu Jasamit , Young Elizabeth , O’Donovan Mary-Ann
Published: April 2022

This paper presents a case study of the Inclusive Research Network (IRN), an inclusive research group founded by the Centre for Disability Studies, contributing to a National Disability Research Agenda (NDRA) setting project. IRN members with ID carried out their own consultations with disability stakeholders. This paper sets out the experiences of the IRN members in facilitating focus groups as part of phase 2 of the NDRA project. As this project took place during the COVID-19 pandemic and state lockdowns, this paper also provides a reflection on new ways of carrying out research and provides an insight into the process for other groups seeking to engage in inclusive research of this nature in a challenging and changing environment. IRN members showed great interest in being involved in the project, and their motivations aligned with the project’s objectives. Facilitating the focus groups on Zoom took some preparation and posed accessibility challenges but enabled a more diverse group of people to attend. IRN members with ID improved their online facilitation skills and confidence but concluded that they preferred working face to face. IRN members overcame the challenges presented by online focus groups, and their involvement added value to the NDRA project. Researchers are encouraged to continue practicing inclusive research in times of remote working and virtual interaction and to share their experiences for mutual learning.
Effects of Network Size, Diversity, and Interaction Frequency on Individual Creativity: A Study from South Korea
Authors: Noh Grimm
Published: April 2022

Creativity is becoming a necessary core competence in nearly all businesses today, and firms are striving to find ways to promote the creativity of employees. This study aimed to analyze the relationships between an individual actor’s network characteristics and the actor’s creativity. More specifically, this study assumed that actors qualitatively differentiate between their global and local networks depending on whether they perceive the other actors in the network as mere acquaintances or trusted persons to discuss life’s important issues with. This study used large-scale survey data collected from South Korea to empirically analyze the hypothesized relationships between network characteristics and creativity. The empirical analysis of the survey data showed that the size and diversity of the global network were positively related to creativity. However, the positive effect of global network diversity decreased with an increase in the size of the network. In the local network, frequent interactions had a positive effect on creativity, while the diversity of the local network had a moderate negative effect on creativity. Implications for the creativity literature are discussed.
Spatiotemporal Variation of Groundwater Extraction Intensity Based on Geostatistics—Set Pair Analysis in Daxing District of Beijing, China
Authors: Li Chen , Men Baohui , Yin Shiyang
Published: April 2022

In this paper, the authors studied the impact of human activities on the groundwater environment to reduce the impacts such activities for sustainable groundwater use. The authors took the monthly water table depth data of 32 long-term observation wells in the Daxing District of Beijing from 1986 to 2016 as samples. The authors used seven interpolation methods in the statistics module of ArcGIS by comparing the average error (ME) and root mean square error (RMSE) between the measured and predicted values so that the authors can select the best interpolation method. Using the geostatistical variogram model variation, the authors analyzed the nugget effect through time in the study area. On the basis of the set pair analysis, the main factors causing the increase in groundwater exploitation intensity were quantitatively evaluated and identified. The results were as follows. (1) After comparing the simulation accuracy of the seven interpolation methods for water table depth, ordinary Kriging interpolation was selected as the best interpolation model for the study area. (2) The spatial correlation of the water table depth gradually weakened, and the nugget effect from 2006 to 2016 was 25.92% (>25%). The data indicated that human groundwater exploitation activities from 2006 to 2016 greatly influenced the spatial correlation of the water table depth. (3) The average mining intensity of groundwater from 2006 to 2016 was medium (Level II), and a bleak gradual deterioration trend was observed. The evaluation results of the subtraction set pair potentials in 2010 and 2013, the years of key regulation of groundwater exploitation intensity, are partial negative potential and negative potential, respectively. In 2010, three indicators had partial negative potential: industrial product, tertiary industry product, and irrigated field area. In 2013, five indicators were in negative potential: irrigated area, vegetable area, facility agricultural area, fruit tree area, and the number of wells. Herein, the spatial and temporal variations in the water table depth of the study area are analyzed using a geostatistical method. Moreover, the influence of each water part on the groundwater exploitation intensity is further diagnosed and evaluated based on set pair analysis. The obtained results can provide a theoretical and methodological reference for the sustainable utilization of groundwater in regions where groundwater is the main water supply source, providing a basis for industrial regulation policies in the region.
Sustainable Heating and Cooling Management of Urban Quarters
Authors: Kugler Till , Schittenhelm Corinna , Volkmer Stephan , Ryba Meinhard , Moormann Christian , Kurth Detlef , Koenigsdorff Roland
Published: April 2022

An important component for a renewable and sustainable heat energy supply is the consideration of urban quarters. For this purpose, the locally available energy sources, a local energy generation system, and the energy distribution in urban quarters should be considered. In the IWAES project presented here, a bidirectional low-temperature heat network was developed, thus it falls into the category of fifth-generation heat networks. It also makes use of existing urban water management infrastructure. The innovative concept is based on the approach of modifying sewers so that they can transport thermal energy between users in the same quarter and extract thermal energy from wastewater. The overall goal is to generate thermal energy and balance the different thermal needs. This is particularly useful in mixed-use quarters, as the peak loads of different uses occur at different times. The supply concept also envisages integrating other thermal energy sources available in the quarter as well as storage options into the supply concept. As a framework for the technical aspects, a precise urban planning concept is needed that provides the legal framework for land use and urban development and coordinates and implements the developed concept—through so-called energy master planning. A life cycle assessment shows the ecological impact of the developed concept compared to a conventional energy solution. It also shows the savings potential of the developed concept compared to an urban quarter supplied conventionally with heating and de-centrally with cooling. The assessment outlines the dual use of the pre-existing infrastructure, such as the wastewater system, significantly reduces CO2 equivalents. Another result is that the sustainability of the system depends significantly on the used mix of electric sources.
Impact of Wind Farm Integration on LMP in Deregulated Energy Markets
Authors: Patil Ganesh Sampatrao , Mulla Anwar , Ustun Taha Selim
Published: April 2022

The integration of renewable energy generation affects the operating characteristics of a power system, such as electric losses, voltage profile, generation cost, system stability, and reliability of the system. The installation of renewable energy generation units in non-optimal locations may increase system losses, costs, voltage fluctuations, etc. The main hurdle in integrating renewable energy generation units with an existing electrical grid is the uncertainty of renewable sources. This paper presents the impact of wind farm integration on the system economy in a wind-integrated deregulated power market. The importance of deregulation in terms of the system generation cost, bus voltage profile, and locational marginal pricing (LMP) are also studied in this work. LMP is the main parameter responsible for handling the system economy (i.e., profit of generating units and profit of customers). Considering the variable nature of wind flow, three different real-time wind speed datasets are used to validate this work. Bus sensitivity factor (BSF) is considered for equating the optimal position of the wind farm in the integrated system. Five different optimization techniques, i.e., sequential quadratic programming (SQP), artificial bee colony (ABC) algorithms, particle swarm optimization (PSO), ant colony optimization (ACO) algorithm, and slime mold algorithm (SMA), are introduced to solve the optimal power flow problem. The SMA and ACO are used for the first time in this type of economic assessment (i.e., impact valuation of LMP) in a deregulated power system, which is the novelty of this work. The entire work is performed in a modified IEEE 30 bus test system.
Improving Professional Skills of Pre-Service Teachers Using Online Training: Applying Work-Integrated Learning Approaches through a Quasi-Experimental Study
Authors: Bamrungsin Phanommas , Khampirat Buratin
Published: April 2022

Preparing preservice teachers for professional engagement is important for teacher education and has received much attention over the past decades. Therefore, finding effective training and coaching to improve the professional skills of preservice teachers (PSTs) is of great importance. This study developed a proactive online training program (POTP) based on a model of work-integrated learning (WIL) activities and teacher education. The objective was to evaluate the effectiveness of the POTP in improving PSTs’ professional skills. The participants consisted of 83 PSTs in an education program from two universities in Thailand. This study comprises three phases: phase I, the development of the POTP; phase II, a quasi-experimental study with a pretest-posttest design; and phase III, a focus group discussion. The findings demonstrated that PSTs in the group in which the POTP was implemented exhibited increased professional skill development compared to the PSTs in the control group, i.e., without the POTP. Analysis from the focus group confirmed that participants gained knowledge and satisfying online tools, and they were found to have better skills. They also revealed that the POTP not only improved professional skills but also enhanced the inspiration and confidence of the PSTs and supported their life and career goals and preparation. Therefore, educators, practitioners, and policymakers involved in pedagogical content knowledge development in teacher education programs can apply the POTP and assessment models proposed in this work to develop essential soft skills for PSTs and to better prepare them for their careers as teachers in the 21st-century digital era.