bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chondroitin and glucosamine sulphate reduced proinflammatory molecules in the DRG and improved axonal function of injured sciatic nerve of rats
Authors: Olaseinde Olutayo Folajimi , Owoyele Bamidele Victor 
Published: February 2022

AbstractNeuropathic pain (NP) is an abnormality resulting from lesion or damage to parts of the somatosensory nervous system. It is linked to defective quality of life and often poorly managed. Due to the limited number of approved drugs, limited efficacy and side effects associated with the approved drugs, drugs or drug combinations with great efficacy and very minimal or no side effects will be of great advantage in managing NP. This study aimed at investigating the synergistic antinociceptive effects of the combination of glucosamine sulphate (GS) (240 mg/kg) and chondroitin sulphate (CS) (900 mg/kg) in chronic constriction injury (CCI)-induced neuropathy in rats. Forty-two Wistar rats were randomly distributed into seven groups (n = 6). Sciatic nerve was ligated with four loose ligatures to induce NP. Effects of drugs were examined on stimulus and non-stimulus evoked potentials, expression of dorsal root ganglia (DRG) pain modulators and structural architecture of DRG. Oral administration of GS and CS for 21 days reduced hyperalgesia, allodynia, sciatic nerve functional aberration and DRG pain modulators. Histopathology and immunohistochemistry revealed restoration of structural integrity of DRG. Our result showed that the combination of GS and CS produced antinociceptive effects by attenuating hyperalgesia, allodynia and downregulation of NP mediators. GS and CS additionally produced synergistic analgesic effect over its individual components.
Structural control of a novel hierarchical porous carbon material and its adsorption properties
Authors: Cai Li-Feng , Zhan Jie-Ming , Liang Jie , Yang Lei , Yin Jie 
Published: February 2022

AbstractNovel hierarchical porous carbon materials (HPCs) were fabricated via a reactive template-induced in situ hypercrosslinking procedure. The effects of carbonization conditions on the microstructure and morphology of HPCs were investigated, and the adsorption of methylene blue (MB) on HPCs was explored. The as-prepared HPCs has a hierarchical micro-, meso- and macropore structure, which results from the overlap of hollow nanospheres possessing microporous shells and macroporous cavities. The carbonization temperature, carbonization time and carbonization heating rate played important roles in tailoring the nanostructures of HPCs. The BET specific surface area and micropore specific surface area can reach 2388 m2 g−1 and 1892 m2 g−1, respectively. Benefitting from the well-developed pore structure, the MB removal efficiency can exceed 99% under optimized conditions. The adsorption kinetics and thermodynamics can be well described by a pseudo-second-order model and Langmuir model, respectively. Furthermore, such adsorption was characterized by a spontaneous endothermic process.
Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain–computer interface
Authors: Khalil Khurram , Asgher Umer , Ayaz Yasar 
Published: February 2022

AbstractThe brain–computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.
High-temperature analysis of optical coupling using AlGaAs/GaAs LEDs for high-density integrated power modules
Authors: Madhusoodhanan Syam , Sabbar Abbas , Tran Huong , Lai Pengyu , Gonzalez David , Mantooth Alan , Yu Shui-Qing , Chen Zhong 
Published: February 2022

AbstractA low-temperature co-fired ceramic (LTCC)-based optocoupler design is demonstrated as a possible solution for optical isolation in high-density integrated power modules. The design and fabrication of LTCC based package are discussed. Commercially available aluminum gallium arsenide/gallium arsenide (AlGaAs/GaAs) double heterostructure is used both as emitter and photodetector in the proposed optocoupler. A detailed study on the electroluminescence and spectral response of the AlGaAs/GaAs structure is conducted at elevated temperatures. The material figure of merit parameter, D*, is calculated in the temperature range 77–800 K. The fabricated optocoupler is tested at elevated temperatures, and the results are presented.
A reassessment of Jackson’s checklist and identification of two Down syndrome sub-phenotypes
Authors: Locatelli Chiara , Onnivello Sara , Gori Caterina , Ramacieri Giuseppe , Pulina Francesca , Marcolin Chiara , Vianello Renzo , Vione Beatrice , Caracausi Maria , Pelleri Maria Chiara , Vitale Lorenza , Pirazzoli Gian Luca , Cocchi Guido , Corvaglia Luigi , Strippoli Pierluigi , Antonaros Francesca , Piovesan Allison , Lanfranchi Silvia 
Published: February 2022

AbstractDown syndrome (DS) is characterised by several clinical features including intellectual disability (ID) and craniofacial dysmorphisms. In 1976, Jackson and coll. identified a checklist of signs for clinical diagnosis of DS; the utility of these checklists in improving the accuracy of clinical diagnosis has been recently reaffirmed, but they have rarely been revised. The purpose of this work is to reassess the characteristic phenotypic signs and their frequencies in 233 DS subjects, following Jackson's checklist. 63.77% of the subjects showed more than 12 signs while none showed less than 5, confirming the effectiveness of Jackson's checklist for the clinical diagnosis of DS. An association between three phenotypic signs emerged, allowing us to distinguish two sub-phenotypes: Brachycephaly, short and broad Hands, short Neck (BHN), which is more frequent, and "non-BHN". The strong association of these signs might be interpreted in the context of the growth defects observed in DS children suggesting decreased cell proliferation. Lastly, cognitive assessments were investigated for 114 subjects. The lack of association between the presence of a physical sign or the number of signs present in a subject and cognitive skills disproves the stereotype that physical characteristics are predictive of degree of ID.
Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks
Authors: Le William Trung , Vorontsov Eugene , Romero Francisco Perdigón , Seddik Lotfi , Elsharief Mohamed Mortada , Nguyen-Tan Phuc Felix , Roberge David , Bahig Houda , Kadoury Samuel 
Published: February 2022

AbstractIn radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life. Deep learning offers an advantage over traditional radiomics for medical image processing by learning salient features from training data originating from multiple datasets. However, while their large capacity allows to combine high-level medical imaging data for outcome prediction, they lack generalization to be used across institutions. In this work, a pseudo-volumetric convolutional neural network with a deep preprocessor module and self-attention (PreSANet) is proposed for the prediction of distant metastasis, locoregional recurrence, and overall survival occurrence probabilities within the 10 year follow-up time frame for head and neck cancer patients with squamous cell carcinoma. The model is capable of processing multi-modal inputs of variable scan length, as well as integrating patient data in the prediction model. These proposed architectural features and additional modalities all serve to extract additional information from the available data when availability to additional samples is limited. This model was trained on the public Cancer Imaging Archive Head–Neck-PET–CT dataset consisting of 298 patients undergoing curative radio/chemo-radiotherapy and acquired from 4 different institutions. The model was further validated on an internal retrospective dataset with 371 patients acquired from one of the institutions in the training dataset. An extensive set of ablation experiments were performed to test the utility of the proposed model characteristics, achieving an AUROC of$$80\%$$80%,$$80\%$$80%and$$82\%$$82%for DM, LR and OS respectively on the public TCIA Head–Neck-PET–CT dataset. External validation was performed on a retrospective dataset with 371 patients, achieving$$69\%$$69%AUROC in all outcomes. To test for model generalization across sites, a validation scheme consisting of single site-holdout and cross-validation combining both datasets was used. The mean accuracy across 4 institutions obtained was$$72\%$$72%,$$70\%$$70%and$$71\%$$71%for DM, LR and OS respectively. The proposed model demonstrates an effective method for tumor outcome prediction for multi-site, multi-modal combining both volumetric data and structured patient clinical data.
A new approach for explosion accident prevention in chemical research laboratories at universities
Authors: Fukuoka Koji , Furusho Masao 
Published: February 2022

AbstractOver the years, many accidents have occurred during chemical experiments in laboratories around the world. However, the methods of investigating and analysing accidents that have occurred at universities have not been consolidated, and the lessons learned from these accidents have not been shared. In this study, accident investigation reports of explosions in chemistry laboratories at two universities were analysed with an analysis tool based on the software/hardware/environment/liveware (SHEL) model. As a result, university accidents were classified as epidemiological models, and it became clear that the contributing factors to the accidents, which were investigated and analysed using the SHEL model, can be used as learning experiences and therefore applied for the prevention of accidents at other universities. Universities around the world need to come together to formulate research and analysis methods, rules for creating accident reports, etc. and provide a place for sharing information that will enable them to make use of the lessons learned from all kinds of accidents.
Advanced removal of Reactive Yellow 84 azo dye using functionalised amorphous calcium carbonates as adsorbent
Authors: Brinza Loredana , Maftei Andreea Elena , Tascu Sorin , Brinza Florin , Neamtu Mariana 
Published: February 2022

AbstractTwo environmentally friendly organics (ethylenediaminetetraacetic acid, EDTA and its easier biodegradabe isomer, ethylenediamine-N, N′-disuccinic acid, EDDS) were used to dope calcium carbonate (CC) nanoparticles intending to increase their adsorptive properties and evaluate adsorption performance (uptake capacity and removal efficiency) for the persistent Reactive Yellow 84 azo dye. Easily synthesized nanomaterials were fully characterized (morphology and size, mineralogy, organic content, surface area, pore size and hydrodynamic diameter). RY84 removal was performed using two consecutive processes: photodegradation after adsorption. The CC-EDTA particles were most efficient for dye removal as compared to the plain and CC-EDDS particles. Adsorption kinetics and isotherms were considered for the CC-EDTA system. 99% removal occurred via adsorption on 1 g/L of adsorbent at 5 mg/L dye concentration and pH of 8 and it decreased to 48% at 60 mg/L. Maximum uptake capacity as described by Langmuir is 39.53 mg/g. As post-adsorption, under UVA irradiation, in the presence of 40 mmol/L H2O2, at dye concentration of 10 mg/L the highest degradation was 49.11%. Substantial decrease of adsorption (ca. 4 times) and photodegradation (ca. 5 times) efficiencies were observed in wastewater effluent as compared to distilled water. The results have important implications to wastewater treatments and appropriate decisions making for the choice of treatment process, process optimization and scaling up to pilot and industrial levels.
Correlation between antibiotic consumption and the occurrence of multidrug-resistant organisms in a Malaysian tertiary hospital: a 3-year observational study
Authors: Tan Sin Yee , Khan Rahela Ambaras , Khalid Khairil Erwan , Chong Chun Wie , Bakhtiar Athirah 
Published: February 2022

AbstractInappropriate use of antibiotics has been shown to contribute to the occurrence of multidrug-resistant organisms (MROs). A surveillance study was performed in the largest tertiary care hospital in Kuala Lumpur, Malaysia, from 2018 to 2020 to observe the trends of broad-spectrum antibiotics (beta-lactam/beta-lactamases inhibitors (BL/BLI), extended-spectrum cephalosporins (ESC), and fluoroquinolones (FQ)) and antibiotics against MRO (carbapenems, polymyxins, and glycopeptides) usage and the correlation between antibiotic consumption and MROs. The correlation between 3-year trends of antibiotic consumption (defined daily dose (DDD)/100 admissions) with MRO infection cases (per 100 admissions) was determined using a Jonckheere-Terpstra test and a Pearson’s Correlation coefficient. The antimicrobial resistance trend demonstrated a positive correlation between ESC and FQ towards the development of methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum beta-lactamase (ESBL)-producing Klebsiella spp, ESBL-producing Escherichia coli (E. coli), and MRO Acinetobacter baumannii (A. baumannii). Increasing carbapenem consumption was positively correlated with the occurrence of ESBL-producing Klebsiella spp and E. coli. Polymyxin use was positively correlated with ESBL-producing Klebsiella spp, MRO A. baumannii, and carbapenem-resistant Enterobacteriaceae. The findings reinforced concerns regarding the association between MRO development, especially with a surge in ESC and FQ consumption. Stricter use of antimicrobials is thus crucial to minimise the risk of emerging resistant organisms.
Warm deep-sea temperatures across Eocene Thermal Maximum 2 from clumped isotope thermometry
Authors: Agterhuis Tobias , Ziegler Martin , de Winter Niels J. , Lourens Lucas J. 
Published: February 2022

AbstractThe early Eocene hothouse experienced highly elevated atmospheric CO2 levels and multiple transient global warming events, so-called hyperthermals. The deep ocean constitutes an assumed setting to estimate past global mean temperatures. However, available deep-sea temperature reconstructions from conventional benthic foraminiferal oxygen isotopes and magnesium/calcium ratios rely on uncertain assumptions of non-thermal influences, associated with seawater chemistry and species-specific physiological effects. Here we apply the carbonate clumped isotope thermometer, a proxy not governed by these uncertainties, to evaluate South Atlantic deep-sea temperatures across two hyperthermal events in the early Eocene (Eocene Thermal Maximum 2/H1 and H2; ~54 Myr ago). Our independent reconstructions indicate deep-sea temperatures of 13.5 ± 1.9 °C (95% CI) for the background conditions and average hyperthermal peak temperatures of 16.9 ± 2.3 °C (95% CI). On average, these absolute temperatures are three degrees warmer than estimates from benthic oxygen isotopes. This finding implies a necessary reassessment of (1) the Eocene seawater isotope composition and (2) pH changes in the deep ocean and its potential influence on benthic foraminiferal oxygen isotope records.
Optimal battery cycling strategies in workplaces with electric vehicle chargers, energy storage systems and renewable energy generation
Authors: Toosi Hooman Ekhteraei , Merabet Adel , Al‐Durra Ahmed , Swingler Andrew 
Published: February 2022

AbstractEfficient unit commitment strategy in a modern workplace facilitated with electric vehicle (EV) chargers and energy storage systems requires implementation of optimal battery cycling for both local storage system and electric vehicle batteries. In order to achieve this goal, it is necessary to address the battery health in the energy management strategies of commercial buildings. A fair battery cycling approach that could consider the interests of both parties in a workplace (including the system operator and the EV owners) requires access to detailed information on battery performance and degradation‐associated costs. In this study, a detailed investigation is carried out on the optimal battery cycling in a workplace that is facilitated with an EV charging station, energy storage system and renewable energy generation. This is carried out by employment of a tailored unit commitment model that can address the battery health for EVs, individually. This study illustrates how a business owner and the employees that own electric vehicles can benefit from bidirectional battery cycling in an equitable way without compromising their financial interests in the energy market.
Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU‐CNN
Authors: Qu Zhaoyang , Bo Xiaoyong , Yu Tong , Liu Yaowei , Dong Yunchang , Kan Zhongfeng , Wang Lei , Li Yang 
Published: February 2022

AbstractTaking account of the fact that the existing knowledge‐driven detection process for false data injection attacks (FDIAs) has been in a passive detection state for a long time and ignores the advantages of data‐driven active capture of features, an active and passive hybrid detection method for power cyber‐physical systems (CPS) FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed are analyzed. The state estimation algorithm based on non‐negative positive‐definite adaptive Kalman filter (NDAKF) is improved, and a passive detection method of FDIAs is constructed, with similarity Euclidean distance detection and residual detection at its core. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature‐expression ability, an active detection method of FDIAs based on a GRU‐CNN hybrid neural network is proposed. Finally, the results of joint knowledge‐driven and data‐driven parallel detection are used to define a mixed fixed‐calculation formula, and an active and passive hybrid detection method of FDIAs is established, considering the characteristic constraints of the parallel mode.
Spatially variant apodisation for sidelobe suppression in near range radar imagery
Authors: Zhu Rongqiang , Zhou Jianxiong , Fu Qiang 
Published: February 2022

AbstractHigh sidelobes greatly reduce the quality of radar imageries. Under the near range condition, sidelobe suppression becomes more intractable due to the space‐variant characteristic of the wavenumber spectrum. The Spatially variant apodisation (SVA) method is extended for sidelobe suppression in near range radar imageries. The method first multiplies the original image by a compensation term so that the wavenumber spectra of all targets shift to the same central position. Then, a spatially variant weighting scheme with the changeable weighting range is adopted to suppress sidelobes. Based on these two procedures, the method effectively overcomes the effect of the space‐variant characteristic of the wavenumber spectrum. Similar to the original SVA method, the proposed method can greatly suppress the sidelobe while maintaining the mainlobe width. In addition, since it is implemented in the image domain, this method is a post‐processing operation and is very conducive to parallel processing. Both 2‐D and 3‐D measured data verify its effectiveness.
Translanguaging… or trans-foreign-languaging?
Authors: Nijhawan Subin 
Published: February 2022

Abstract
Amidst the demand for multilingual pedagogies that advocate the use of the first language (L1) in Content and Language Integrated Learning (CLIL), this article first investigates the concept of translanguaging as a possible panacea. Translanguaging mainly refers to natural multilingual practices of speakers with multicompetences beyond their dominant language. However, doubts need to be expressed as to whether students without adequate language resources in the foreign language (FL) (henceforth referred to also as L2 intercheangeably) can fully enjoy all the benefits of translanguaging. Thus, the concept of translanguaging was adapted into trans-foreign-languaging (Trans-FL), making its distinctness available for CLIL. During an interventionalist study carried out in a 10th grade CLIL Politics & Economics classroom, where the students’ L2 is English and German, the official school language, is L1, three different models of Trans-FL were designed together with the students as main stakeholders, using triangulated data. The genesis of the three models was reconstructed as thick description in the Appendix, elucidating different intensities of dynamic bilingualism within a natural classroom ecology. Finally, the models were incorporated into one single and comprehensive CLIL teaching model for an affordance-based and differentiated approach that recognizes the needs of various student types. The result is a tangible pedagogy that integrates L1 use into various CLIL contexts as a norm.
Suppression of interaction-induced loss rate coefficient near broad s-wave and p-wave Feshbach resonances by magnetic field
Authors: Lyu Bing-Kuan , Sun Zhi-Xin , Wang Gao-Ren , Cong Shu-Lin 
Published: February 2022

Abstract
Ultracold atoms prepared in excited internal states can escape from the trap through inelastic collisions. A magnetic field can be used to suppress the s-wave loss rate coefficient near an s-wave Feshbach resonance. However, at the temperature of μK the p-wave loss rate coefficient becomes increasingly important as the temperature increases. The unsuppressed p-wave loss rate coefficient can far exceed the suppressed s-wave loss rate coefficient, and severely restricts the reduction of the total loss rate coefficient. We calculate the collision rate coefficients of ultracold 85Rb and 87Rb atoms prepared in excited hyperfine and Zeeman states using the coupled-channel method. We find that in

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there exist broad p-wave Feshbach resonances located near s-wave Feshbach resonances, and find that the s- and p-wave loss rate coefficients can be simultaneously suppressed by a magnetic field in the temperature range from 1 to 30 μK. In particular, for

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the proportion of the s- and p-wave loss rate coefficients in the total loss rate coefficient can be well manipulated by adjusting the magnetic field.