bluuebunny/crossref_metadata_2025_split huggingface dataset:
Downregulated exosomal microRNA-148b-3p in cancer associated fibroblasts enhance chemosensitivity of bladder cancer cells by downregulating the Wnt/β-catenin pathway and upregulating PTEN
Authors: Shan Guang , Zhou Xike , Gu Juan , Zhou Daoping , Cheng Wei , Wu Huaiguo , Wang Yueping , Tang Tian , Wang Xuedong 
Published: January 2021

Abstract
Objective
Exosomes derived from cancer-associated fibroblasts (CAFs) are known as important drivers of tumor progression. Previously, microRNA (miR)-148b-3p has been found to be upregulated in bladder cancers as well as in body fluids (blood, urine) of bladder cancer patients. Here, we aimed to explore the role of CAF-derived exosome miR-148b-3p in bladder cancer progression and chemosensitivity.

Methods
Transwell, MTT, flow cytometry and colony formation assays were applied to assess the effects of CAF-derived exosomes on bladder cancer cell metastasis, epithelial-mesenchymal transition (EMT) and chemosensitivity. A dual luciferase reporter assay was employed to evaluate the targeting relationship between miR-148b-3p and PTEN. Gain- and loss- of function assays were conducted to explore the roles of miR-148b-3p and PTEN in the behavior of bladder cancer cells. The role of PTEN in the metastasis, EMT and chemosensitivity of bladder cancer cells was assessed both in vivo and in vitro.

Results
We found that CAF-derived exosomes promoted the metastasis, EMT and drug resistance of bladder cancer cells. We also found that CAF-derived exosomes could directly transport miR-148b-3p into bladder cancer cells. In a xenograft mouse model we found that CAF-derived exosomes increased miR-148b-3p expression levels and promoted tumor proliferation, metastasis and drug resistance. PTEN was validated as a target of miR-148b-3p. Concordantly, we found that PTEN overexpression inhibited EMT, metastasis and chemoresistance in bladder cancer cells, reversing the tumor promoting effects of miR-148b-3p via the Wnt/β-catenin pathway.

Conclusions
Our results suggest that miR-148b-3p downregulation in CAF-derived exosomes, thereby inhibiting the Wnt/β-catenin pathway and promoting PTEN expression, may offer potential opportunities for bladder cancer treatment.
Knowledge from the original network: restore a better pruned network with knowledge distillation
Authors: Chen Liyang , Chen Yongquan , Xi Juntong , Le Xinyi 
Published: January 2021

AbstractTo deploy deep neural networks to edge devices with limited computation and storage costs, model compression is necessary for the application of deep learning. Pruning, as a traditional way of model compression, seeks to reduce the parameters of model weights. However, when a deep neural network is pruned, the accuracy of the network will significantly decrease. The traditional way to decrease the accuracy loss is fine-tuning. When over many parameters are pruned, the pruned network’s capacity is reduced heavily and cannot recover to high accuracy. In this paper, we apply the knowledge distillation strategy to abate the accuracy loss of pruned models. The original network of the pruned network was used as the teacher network, aiming to transfer the dark knowledge from the original network to the pruned sub-network. We have applied three mainstream knowledge distillation methods: response-based knowledge, feature-based knowledge, and relation-based knowledge (Gou et al. in Knowledge distillation: a survey. arXiv:200605525, 2020), and compare the result to the traditional fine-tuning method with grand-truth labels. Experiments have been done on the CIFAR100 dataset with several deep convolution neural network. Results show that the pruned network recovered by knowledge distillation with its original network performs better accuracy than it recovered by fine-tuning with sample labels. It has also been validated in this paper that the original network as the teacher performs better than differently structured networks with same accuracy as the teacher.
Age of smile: a cross-cultural replication report of Ganel and Goodale (2018)
Authors: Yoshimura Naoto , Morimoto Koichi , Murai Mariko , Kihara Yusaku , Marmolejo-Ramos Fernando , Kubik Veit , Yamada Yuki 
Published: January 2021

AbstractSmiling is believed to make people look younger. Ganel and Goodale (Psychon Bull Rev 25(6):612–616, 10.3758/s13423-017-1306-8, 2018) proposed that this belief is a misconception rooted in popular media, based on their findings that people actually perceive smiling faces as older. However, they did not clarify whether this misconception can be generalized across cultures. We tested the cross-cultural validity of Ganel and Goodale’s findings by collecting data from Japanese and Swedish participants. Specifically, we aimed to replicate Ganel and Goodale’s study using segregated sets of Japanese and Swedish facial stimuli, and including Japanese and Swedish participants in groups asked to estimate the age of either Japanese or Swedish faces (two groups of participants × two groups of stimuli; four groups total). Our multiverse analytical approach consistently showed that the participants evaluated smiling faces as older in direct evaluations, regardless of the facial stimuli culture or their nationality, although they believed that smiling makes people look younger. Further, we hypothesized that the effect of wrinkles around the eyes on the estimation of age would vary with the stimulus culture, based on previous studies. However, we found no differences in age estimates by stimulus culture in the present study. Our results showed that we successfully replicated Ganel and Goodale (2018) in a cross-cultural context. Our study thus clarified that the belief that smiling makes people look younger is a common cultural misconception.
Evaluation of water quantity–quality, floodplain landuse, and land surface temperature (LST) of Turag River in Bangladesh: an integrated approach of geospatial, field, and laboratory analyses
Authors: Tania Afsana Hossen , Gazi Md. Yousuf , Mia Md. Bodruddoza 
Published: January 2021

AbstractRapid urbanization, industrialization, and development activities are posing numerous threats to the rivers of Bangladesh. In the recent decade, Turag River has been exposed to severe threats due to anthropogenic activities. The present research deals with the evaluation of water quantity–quality, landuse and landcover (LULC), and land surface temperature (LST) of Turag River and its floodplain by using an integrated approach of field study, laboratory analysis, and geospatial techniques. LULC mapping of the study area has been done using multi-temporal Landsat satellite images by unsupervised method, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index indices (NDWI) approach. From LULC classification, the study shows that the average reduction of water bodies from 1989 to 2019 is about 60–66%, and the rate of reduction is about 14.25 ha per year. Due to anthropogenic activities and rapid urbanization, a large number of water bodies have been depleting. The average temperature of the river water from filed observed data is 30.23 °C and from image processed data is about 28.32 °C. The minimum and maximum values of pH, EC, TDS, and Eh are 6.87–8.44, 808–1192 μs/cm, 406–739 ppm, − 31 to – 150 mV, respectively. The concentration ranges of cations (Na, Ca, Mg, K, Fe, and Mn) are 85.43–247.60, 28.59–39.47, 11.1–13.58, 5.21–13.26l, .07–1.14, .006–.47 mg/l consecutively. The ranges of anions concentration (HCO3−, Cl−, SO42−, NO3−) are 165.45–388.87, 54.81–194.07, 52.49–289.63, l9.63–126.93 mg/l, respectively. It is evident that HCO3−, NO3−, and Mn exceed the standard limit of Bangladesh Water Quality Standard (BWQS) and World Health Organization (WHO), indicating that the water of the Turag River is deteriorating that is not safe for human consumption and aquaculture purposes. The study will assist to take some effective measures to control the pollution and uphold the water quality and natural riparian processes. So, the outputs of the study will contribute to the future urban landuse planning in the river floodplain area to mitigate ongoing alarming pollution and unplanned industrialization.
Efficacy evaluation of neurofeedback applied for treatment of central neuropathic pain using machine learning
Authors: Saif Mohammed Gamil Mohammed , Hasan Muhammad Abul , Vuckovic Aleksandra , Fraser Matthew , Qazi Saad Ahmed 
Published: January 2021

AbstractBrain-computer interface (BCI) is believed to be the translator of brain signals into actions based on the model, built on the machine learning (ML) algorithms, incorporated in it. This study reports on the performance of various ML algorithms in evaluating efficacy of neurofeedback applied for treatment of central neuropathic pain (CNP). In the first phase of this study, we applied different ML algorithms for classification of electroencephalography (EEG) patterns, associated with CNP, obtained from three groups of participants, during imagined movement of their limbs, named as able-bodied (AB), paraplegic patients with (PWP) and without (PNP) neuropathic pain. In the second phase, we tested the accuracy of BCI-classifier by applying new EEG data obtained from PWP participants who have completed neurofeedback training provided for the management of pain. Support vector Machine (SVM) algorithm gained higher accuracy, with all groups, than the other classifiers. However, the highest classification accuracy of 99 ± 0.49% was obtained with the right hand motor imagery of (AB vs PWP) group and 61 electrodes. In Conclusion, SVM based BCI-classifier achieved high accuracy in evaluating efficacy of neurofeedback applied for treatment of CNP. Results of this study show that the accuracy of BCI changes with ML algorithm, electrodes combinations, and training data set.
Influence of debinding and sintering conditions on the composition and thermal conductivity of copper parts printed from highly loaded photocurable formulations
Authors: Roumanie Marilyne , Flassayer Cécile , Resch Adrien , Cortella Laurent , Laucournet Richard 
Published: January 2021

AbstractMetal 3D printing based on the photopolymerization reaction (Digital Light Processing—DLP) of an organic matrix in which metal particles are embedded is a developing technology. This technology requires a step of resin removal and densification by sintering to obtain a metal part. This process has been applied to copper. Photocurable formulations with a high loading rate of copper powder of 60 vol.% were developed and suitable for DLP printing with thicknesses > 25 µm. Debinding and sintering cycles were investigated on specimens fast cured by gamma irradiation to save materials and time. A debinding in air at 400 °C and sintering in hydrogen lead to a C content of 0.018 wt.%, similar to the raw copper powder and slightly higher oxygen content. The low thermal conductivity of 250 W·m−1·K−1 highlighted the harmful effect of phosphorus from the powder and photoinitiators such as BAPO. The C and O contents and the thermal conductivity measured on copper parts printed by DLP confirm the results obtained on specimens cured by gamma irradiation.
Differential evolution based radial basis function neural network model for reference evapotranspiration estimation
Authors: Majhi Babita , Naidu Diwakar 
Published: January 2021

AbstractThe present study is an effort to examine the capability of a differential evolution based radial basis function neural network (RBFDE) to model weekly reference evapotranspiration (ET0) as a function of climatic parameters in different agro-climatic zones (ACZs) of a moist sub-humid region in East-Central India. The ET0 computed using the empirical equation of Penman–Monteith suggested by the Food and Agricultural Organization (FAO56-PM) is considered as a target variable for investigation. The performance of the proposed RBFDE model is compared with particle swarm optimization based radial basis function (RBFPSO), radial basis function neural network (RBFNN), multilayer artificial neural network (MLANN) models and conventional empirical equations of Hargreaves, Turc, Open-Pan, and Blaney-Criddle. Weekly ET0 estimates that are obtained using RBFDE, RBFPSO, and RBFNN and MLANN are observed to be more consistent than equivalent empirical methods. For a critical analysis of simulation results, mean absolute percentage error (MAPE), root means square error (RMSE), determination coefficient (R2) and Nash–Sutcliffe efficiency factor (NSE) is computed. Low MAPE and RMSE values along with higher R2 and NSE close to 1, obtained with soft computing models exhibit that, soft computing models produce better estimates of ET0 than empirical methods. Among the soft computing models, RBFDE provides improved results as compared to RBFPSO, RBFNN, and MLANN models. This method can be extended for ET0 estimation in other ACZs.
Study of performance parameters and emissions of four stroke CI engine using solketal-biodiesel blends
Authors: Kumar Pardeep , Kumar Shailendra , Shah Saumya , Kumar Sandeep 
Published: January 2021

AbstractEvaluation of performance parameters, emission and brake specific fuel consumption (BSFC) were made for different blends of solketal in soybean biodiesel with 9%, 10%, 12%, 15% of solketal (volume to volume). These experiments were performed on a single-cylinder water-cooled diesel engine at different speeds and 50% load conditions. In comparison to pure diesel, it was observed that soybean biodiesel and its blends with solketal were higher BSFC. On the addition of solketal to biodiesel higher oxides of nitrogen (NOx) and carbon dioxide (CO2) emissions were reported for all the blends. total hydro carbon (THC) and carbon mono-oxide (CO) emissions were found lower for soybean biodiesel and it’s all four blends as compared to pure diesel and further decreases with the increase in solketal percentage in a blend. A decrease in CO and THC emissions were also observed with the increase in percentage of Solketal fuels. Solketel was found suitable and as an effective additive for biodiesel blends.
Luminescent and thermal properties of novel orange–red emitting MgNb2O6:Sm3+ phosphors for displays, photo catalytic and sensor applications
Authors: Basavaraju N. , Prashantha S. C. , Nagabhushana H. , Naveen Kumar A. , Chandrasekhar M. , Shashi Shekhar T. R. , Ravikumar C. R. , Anil Kumar M. R. , Surendra B. S. , Nagaswarupa H. P. 
Published: January 2021

AbstractNovel Sm3+ doped columbite-type orthorhombic structured MgNb2O6 (MNO) orange-red emitting phosphors were prepared by solution combustion method using ODH as a fuel. The powder phase purity, particle morphology, size, elemental composition, luminescence properties, photocatalytic behaviors and electrochemical studies of prepared samples were studied in detail. Photoluminescence emission spectra of MNO:Sm3+ nanophosphors show all the characteristic emissions of Sm3+ cations corresponds to the transitions 4G5/2 → 6Hj/2(j=5,7,9,11) when excited at 463 nm energy. Among these the strongest emission peak was at 608 nm which corresponds to 4G5/2 → 6H7/2 transition of Sm3+ cations in the host lattice. The luminescence quenching was confirmed by the dipole–dipole interaction among Sm3+ ions. As a result of J-O analysis the branching ratio (~ 58% > 50%) show that the phosphor was highly suitable for color display devices. Photocatalytic activity of MNO:Sm3+ (5 mol%) under UV light shows 99% degradation of AR-88 dye. Electrochemical Impedance Spectroscopy (EIS) confirms the reversibility of the redox reaction, which helps in sensing the presence of paracetamol and alcohol. Thus, MNO:Sm3+ phosphors have great potential applications in display, catalytic, photonic, chemical and thermal sensor applications.
Stress level classification using statistical analysis of skin conductance signal while driving
Authors: Memar Maryam , Mokaribolhassan Amin 
Published: January 2021

AbstractConventionally, multiple physiological signals are used in the field of stress realization. Although many studies have applied various methods in feature selection and classification, a desirable performance has not yet been achieved. This paper presents a novel method of stress level classification using physiological signals during the real-world driving task. Exploring the most reliable analysis method on a comprehensive physiological signal for stress realization has been commonly investigated in various studies. To obtain a high accuracy approach, a proper classification method should be applied to the most relevant physiological signal. In this study, we evaluate the feasibility and effectiveness of the analysis of variance (ANOVA) classifier learner on the single Galvanic Skin Response (GSR) signal. Three levels of stress are taken into account and two independent features including rising time and amplitude are extracted. These two features are extracted from foot and hand GSR signals in three different scenarios for the sake of training. The result indicates that the foot amplitude feature of the GSR signal solely is a reliable source of stress classification with an accuracy rate of 95.83% by applying the ANOVA approach. Accordingly, this methodology can substantially reduce the necessity of resorting to the high number of sensors and the corresponding computational burden associated with signal analysis. Besides, reducing the number of sensors during the measurement procedure would increase drivers’ safety by reducing the interference between human and measurement devices. In this study, the real data collected by Picard and his co-workers are used, available in the PHYSIONET database.
Scientific memory from the early nineties; a common project with professors late János Gergely and Anna Erdei
Authors: Falus Andras , Bajtay Zsuzsa 
Published: January 2021

AbstractBased on the findings of common project 29 years ago, the Scandinavian J. of Immunology accepted and published our paper entitled by “FcγR-Dependent Regulation of the Biosynthesis of Complement C3 by Murine Macrophages: the Modulatory Effect of IL-6” (Bajtay et al. in SJI 35:195–201, 1992). In this report we attempt to review the previous results and evaluate them with our current concepts on the interaction between the actors of adaptive and innate immunity. Let us first to summarize the basic results and consequences from the paper from 1992. Abstract from 1991–1992: The effect of murine IgG isotypes (myeloma proteins) on the gene expression and secretion of the third component of complement (C3) has been studied using the in monocytoid cell line P388D1 and oil-elicited mouse peritoneal macrophages. It is demonstrated that the binding of lgG2a and lgG2b but not IgGl and IgG3 isotypes augments the biosynthesis of C3 both in the presence and in the absence of the phorbol myristate acetate in the case of both cell types. The multifunctional cytokine inlerleukin-6 (IL-6) alone reveals no effect on the gene expression of C3, but facilitates the effectiveness of mouse IgG2a and IgG2b. Confirming the role of FcgRll, a strong up-regulation of gene expression and secretion of C3 was found when macrophages were co-cultured with the F(ab')2 fragment of the FcγRII-specific monoclonal antibody 2.4 G2.
Different Gene Networks are Disturbed by Zika Virus Infection in a Mouse Microcephaly Model
Authors: Chang Yafei , Jiang Yisheng , Li Cui , Wang Qin , Zhang Feng , Qin Cheng-Feng , Wu Qing-Feng , Li Jing , Xu Zhiheng 
Published: January 2021

AbstractThe association of Zika virus (ZIKV) infection with microcephaly has raised alarm worldwide. Their causal link has been confirmed in different animal models infected by ZIKV. However, the molecular mechanisms underlying ZIKV pathogenesis are far from clear. Hence, we performed global gene expression analysis of ZIKV-infected mouse brains to unveil the biological and molecular networks underpinning microcephaly. We found significant dysregulation of the sub-networks associated with brain development, immune response, cell death, microglial cell activation, and autophagy amongst others. We provided detailed analysis of the related complicated gene networks and the links between them. Additionally, we analyzed the signaling pathways that were likely to be involved. This report provides systemic insights into not only the pathogenesis, but also a path to the development of prophylactic and therapeutic strategies against ZIKV infection.
Factors predictive of an academic otolaryngologist's scholarly impact
Authors: Shires Courtney B. , Klug Theodore D. , Meacham Ryan K. , Sebelik Merry E. 
Published: January 2021

ObjectivesDescribe the h index as a bibliometric that can be utilized to objectively evaluate scholarly impact. Identify which otolaryngology subspecialties are the most scholarly. Describe if NIH funding to one's choice of medical school, residency, or fellowship has any impact on one's scholarly output. Determine other factors predictive of an academic otolaryngologist's productivity.Study designAnalysis of bibliometric data of academic otolaryngologists.MethodsActive grants from the National Institutes of Health (NIH) to otolaryngology departments were ascertained via the NIH Research Portfolio Online Reporting Tools Expenditures and Reports database. Faculty listings from these departments were gleaned from departmental websites. H index was calculated using the Scopus database.ResultsForty‐seven otolaryngology programs were actively receiving NIH funding. There were 838 faculty members from those departments who had a mean h index of 9.61. Otology (h index 12.50) and head and neck (h index 11.96) were significantly (P < 0.0001) more scholarly than the rest of subspecialists. H index was significantly correlative (P < 0.0001) with degree of NIH funding at a given institution. H index was not significantly higher for those that attended medical school (P < 0.18), residency (P < 0.16), and fellowship (P < 0.16) at institutions with NIH funding to otolaryngology departments.ConclusionsH index is a bibliometric that can be used to assess scholarly impact. Otology and head and neck are the most scholarly subspecialists within otolaryngology. NIH funding to an individual's medical school, residency, or fellowship of origin is not correlative with one's scholarly impact, but current institutional affiliation and choice of subspecialty are.
Integration of full-length transcriptomics and targeted metabolomics to identify benzylisoquinoline alkaloid biosynthetic genes in Corydalis yanhusuo
Authors: Xu Dingqiao , Lin Hanfeng , Tang Yuping , Huang Lu , Xu Jian , Nian Sihui , Zhao Yucheng 
Published: January 2021

AbstractCorydalis yanhusuoW.T. Wang is a classic herb that is frequently used in traditional Chinese medicine and is efficacious in promoting blood circulation, enhancing energy, and relieving pain. Benzylisoquinoline alkaloids (BIAs) are the main bioactive ingredients inCorydalis yanhusuo. However, few studies have investigated the BIA biosynthetic pathway inC. yanhusuo, and the biosynthetic pathway of species-specific chemicals such as tetrahydropalmatine remains unclear. We performed full-length transcriptomic and metabolomic analyses to identify candidate genes that might be involved in BIA biosynthesis and identified a total of 101 full-length transcripts and 19 metabolites involved in the BIA biosynthetic pathway. Moreover, the contents of 19 representative BIAs inC. yanhusuowere quantified by classical targeted metabolomic approaches. Their accumulation in the tuber was consistent with the expression patterns of identified BIA biosynthetic genes in tubers and leaves, which reinforces the validity and reliability of the analyses. Full-length genes with similar expression or enrichment patterns were identified, and a complete BIA biosynthesis pathway inC. yanhusuowas constructed according to these findings. Phylogenetic analysis revealed a total of ten enzymes that may possess columbamine-O-methyltransferase activity, which is the final step for tetrahydropalmatine synthesis. Our results span the whole BIA biosynthetic pathway inC. yanhusuo. Our full-length transcriptomic data will enable further molecular cloning of enzymes and activity validation studies.