bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the evolution and the driving forces of land use classification for production, living and ecological space in China's Qilian Mountains Nature Reserve from 2000 to 2020
Authors: Wang Yaobin , Zhao Ruitao , Li Ying , Yao Rong , Wu Ruoxue , Li Wenlin 
Published: September 2022

Abstract
This research constructed a production-living-ecological function evaluation index system, used entropy method, land use transfer matrix, kernel density analysis and geodetector to evaluate the production-living-ecological functions of 13 districts and counties in China's Qilian Mountains for the period 2000-2020. The results of the study showed: There are obvious spatial differences in the function of production-living-ecological in the Qilian Mountains. The production and living function index are rising. The most advantaged areas are in the northern part of the research area where the terrain is flat and transportation is convenient. The ecological function index rises, falls, then rises again. The high-value area is mainly located in the south of the study area and its ecological function is intact. The Qilian Mountains are dominated by ecological spatial distribution; the area of production and living space increased throughout the study period. The increase in production space mainly comes from ecological space and unused land; the increase in living space mainly comes from production space and unused land. Ecological degradation is a serious problem in northern Qilian County and central Menyuan County. Among geographical factors, altitude has a significant impact on the evolution of living space; other geographical factors have a limited impact on functional space. Population density is the main factor in changing the areas of production and ecological space. The interaction affects production, living and ecological space more than any individual factor.
Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News
Authors: Zair Bouzidi , Abdelmalek Boudries , Mourad Amad 
Published: September 2022

Abstract
Networking continues to increase day by day to invest increasingly our daily, creating huge volume of various and precise data: it is not easy to collect content, especially in crisis times. We focus on Smart Education proposed as primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieving content efficiently: CNN is used to extract meaningful features from multiple sources, enabling to have qualitative and sure information, notably with an efficient fake news security, and LSTM is used to maintain long-term dependencies in the extracted features with recurrent connections.
This model has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach makes it possible to integrate artificial intelligence technologies, deep learning, social media and detecting or avoiding fake news into the crisis management model. It is based on an extension of our previous approach, namely disaster management based on short-term memory and education: this experience constitutes a background for this model. It combines representation training with awareness and education, while retrieving pattern information by combining various search results from multiple sources. We have extended it to improve our disaster management model and evaluate it in the case of Covid-19 while obtaining promising results, through past programs and experience that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pusuit of Environmental Sustainability.
Theme: Impact of early food diversification on the composition of the gut microbiota in infants during the first 200 days of life from three municipalities in the District of Abidjan
Authors: jerome mouroufie abo kouadio 
Published: September 2022

Abstract
The gastrointestinal microbiota plays a crucial role in host health and disease through its impact on nutrition. Gut microbial composition is linked to different diets, but an association of gut microbiota with different infant diets has been lightly studied. In this work, we compared the faecal microbiota of breastfed infants receiving dietary diversification (LM+DA), infants fed formula milk combined with complementary foods (LA+DA) and infants on a mixed breastfeeding diet accompanied by food supplements (LM+LA+DA). Differences in gut microbiota were found between the three groups using Illumina high-throughput sequencing.
Actinobacteria and Bacteroides are the most dominant phyla in all three groups. However, Firmicutes and Bacteroidetes are less represented in infants on LA+DA diets. For infants on the LM+DA diet, Bifidobacterium (17.84%), Escherichia-Shigella (9.37%) and Streptococcus (7.4%) dominate more, whereas in LA+DA infants, the domination is observed with Bifidobacterium(24.33%), Escherichia-Shigella (14.35%). Finally, LM+LA+DA infants are also Bifidobacterium(14.99%), Escherichia-Shigella (9.17%) and Streptococcus (7.68%) which dominate (p=0.05). In addition, at age level, there is a significant difference (p<0.05). Between 0-119 daysBifidobacterium (15.70%), Enterobacteriaceae (11.28%) dominate; between 120-179 days Bifidobacterium (20.62%) and Enterobacteriaceae (8.95%) which also dominate, but in lower proportions compared to infants of 0-119 days. Finally, at 200 days, Bifidobacterium (14%) and Enterobacteriaceae (9.31%) also dominate, but in low proportions.
ENAH regulates survival, self-renewal, and migration of patient-derived primary glioblastoma neurospheres
Authors: Rock Mitchell , Bryant Jean-Paul , Walbridge Stuart , Dalmage Mahalia , Namagiri Sriya , Chowdhury Ashis , Edwards Nancy , Jackson Sadhana , Ray-Chaudhury Abhik , Heiss John , Kumbar Sangamesh , Banasavadi-Siddegowda Yeshavanth Kumar 
Published: September 2022

Abstract
Purpose: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. Despite standard treatment, the median survival of GBM patients is only 15 months. Enabled homolog (ENAH) is a member of the Ena/VASP protein family and regulates actin dynamics. ENAH has been implicated in a wide variety of cancers. But its role in GBM is less explored. We sought to understand the role of ENAH in GBM tumor biology.
Methods: Expression of ENAH in GBM tumor specimens was assessed by immunohistochemistry (IHC). Patient-derived primary GBM neurosphere (GBMNS) models transfected with scrambled or target-specific ENAH siRNA were subjected to viability, apoptosis, neurosphere formation, western blot, transwell migration, and F-actin immunofluorescence assays. In vivo, we implanted mice with ENAH-intact and -depleted GBMNS intracranially and analyzed survival duration.
Results: IHC analysis suggests that the expression of ENAH is higher in GBM tumor samples compared to low-grade gliomas and normal brain tissues. In vitro, ENAH knockdown reduced the viability of GBMNS by more than 50% by inducing apoptosis and significantly decreased the self-renewal ability. Also, we show that depletion of ENAH reduced the migration of GBMNS and demonstrated that ENAH is required for actin polymerization. Further, the expression of AXL-GAS6 was downregulated with the knockdown of ENAH. In vivo, transient depletion of ENAH significantly increased the survival of tumor-bearing mice.
Conclusions: Our results suggest that ENAH is required for survival, self-renewal, and migration of GBMNS. Hence, ENAH can serve as a potential target for GBM therapy.
Research on the impact of urbanization on Climate and Ecological Environment of World Cultural Heritage Site-- A case study of Fuling Mausoleum in Qing Dynasty
Authors: Wang Jing , Wang Xiaoyu , Han Zhigen , Zhang Keqing 
Published: September 2022

Abstract
Taking the Fuling Mausoleum,a world cultural heritage, as the research object, this paper analyzes the impact of urbanization on the climate and ecological environment of the World Cultural Heritage site.Based on the Landsat series remote sensing image data and meteorological station data from 2005 to 2020, the spatial and temporal change characteristics of urban land in the region where the world cultural heritage located are characterized. The temporal and spatial change characteristics and influencing factors of climate change and habitat quality in the study area are quantitatively evaluated and analyzed by GIS technology and Habitat Quality module of InVEST model.The results show that: (1) the urban land in the area where Fuling Mausoleum is located shows a trend of expanding eastward and southward along the existing urban land from 2005 to 2020, and agricultural land and woodland to construction land are the main sources of new urban land, which is in line with the characteristics of urban development and changes in Shenyang in recent years.(2)During the study period, the temperature in the area where the Fuling Mausoleum was located showed a fluctuating upward trend, the average temperature increased by 0.15C / 10a, and the precipitation increased by 102mm/10a. At the same time, NDVI showed a significant negative correlation with the average temperature, and a positive correlation with precipitation as a whole.(3)From 2005 to 2020, the habitat quality of the Fuling Mausoleum heritage site showed an overall downward trend: among them, the average annual decline rate was 0.36% in the areas with high habitat quality levels, 0.14% in the middle regions, and 0.5% in the areas with low habitat quality levels. The heritage site is strongly disturbed by human factors, especially the degradation in the east of Qing Fuling is very obvious, but the distribution of the degraded area is relatively scattered. The low-value and non-degraded areas of habitat degradation index are mainly located in the heritage area and the eastern mountain region.
Efficacy of plasma atherogenic index in predicting malignancy in the presence of Prostate Imaging-Reporting and Data System 3 (PI-RADS 3) prostate lesions
Authors: Senel Samet , Ceviz Kazim , Kasap Yusuf , Tastemur Sedat , Olcucuoglu Erkan , Uzun Emre , Polat Muhammed Emin , Koudonas Antonios 
Published: September 2022

Abstract
Purpose:The prevalence of the clinically significant prostate cancer (csPCa) among the Prostate Imaging-Reporting and Data System (PI-RADS) 3 cases is estimated to be 16%, which suggests that the indication for biopsy by the combined method may be further refined. Plasma atherogenic index (PAI) was shown to be positively correlated with the presence of malignity in patients with suspicious findings for renal cell cancer and colon cancer in reported studies. In this study, we aimed to evaluate whether there is an association with the presence of malignity in a patient with magnetic resonance imaging (MRI)-visible lesions, classified as PI-RADS 3 and PAI.
Methods:This retrospective study reviewed the data of 139 patients who underwent transrectal ultrasonography (TRUS)-guided systematic and cognitive fusion prostate biopsy for PI-RADS 3 lesions in multiparametric MRI (mpMRI). The patients were divided to two groups as malign (n=33) and benign (n=106) according to pathology results. The association between age, body mass index [BMI], comorbidities, smoking status, prostate specific antigen [PSA], PSA density [PSAD], free/total PSA, prostate weight, lesion diameter, triglyceride value, high density lipoprotein (HDL)-Cholesterol value, PAI value data and presence of malignity were investigated by descriptive and multivariate analysis. Receiver operating characteristic (ROC) curves were created to evaluate the predictive role of the parameters and cut-off values were found.
Results: PSA, PSAD, lesion diameter and PAI value were statistically significantly higher in the malignant group compared to the benign group, the free/total PSA ratio was lower (p<0.001, p<0.001, p=0.001, p=0.025, p<0.001, respectively). In multivariate logistic regression analysis, PSA > 9.9 ng/ml (OR=4.579; 95% Cl=1.455-14.413; p=0.009), free/total PSA < 12.1% (OR=5.851; 95% CI=1.752-9.442; p <0.001), lesion diameter > 13.5 mm (OR=5.695; 95% CI=1.694-19.146; p=0.005) and PAI > 0.13 (OR=3.821; 95% CI=1.208-12084; p=0.022) were identified as independent risk factors for presence of prostate malignancy.
Conclusion: PAI demonstrated a strong association with PCa and additionally proved to be predictive in the discrimination between malignity positive and negative PI-RADS 3 lesions and it may be used as a predictive parameter in the decision of biopsy in patients with PI-RADS 3 lesions.
Efficacy of cooling + oral dexamethasone as primary prevention for hand-foot syndrome associated with liposomal doxorubicin
Authors: Nara Katsuhiko , Taguchi Ayumi , Yamamoto Takehito , Tsuruga Tetsushi , Tojima Yuri , Miyamoto Yuichiro , Tanikawa Michihiro , Sone Kenbun , Mori Mayuyo , Takada Tappei , Suzuki Hiroshi , Osuga Yutaka 
Published: September 2022

Abstract
Purpose: Pegylated liposomal doxorubicin (PLD)-induced hand-foot syndrome (HFS) frequently lowers the quality of life of ovarian cancer patients. Wrist and ankle cooling, having a limited preventive effect, has been the commonest supportive HFS care. In this study, we retrospectively assessed the primary preventive effect of a combination of cooling and oral dexamethasone therapy (cooling + oral Dex) on HFS.
Methods: This study is a single-arm retrospective, observational study. Recurrent ovarian cancer patients were administered PLD ± bevacizumab. We retrospectively examined the efficacy of hands and feet cooling (from the start of PLD to the end) + oral Dex (day 1-5: 8 mg/day, day 6, 7: 4 mg/day) for primary HFS prevention.
Results: This study included 74 patients. The initial dose of PLD was 50 mg/m2 and 40 mg/m2 for 32 (43.2%) and 42 (56.8%) patients, respectively. HFS of Grade ≥ 2 and Grade ≥ 3 developed in five (6.8%) and one (1.4%) patient(s), respectively. The incidence of ≥ Grade 2 and ≥ Grade 3 HFS was much lower than those reported in previous studies. Dose reduction was required in 13 patients (17.6%) mainly because of neutropenia or mucositis; there was no HFS-induced dose reduction. Meanwhile, PLD therapy was discontinued mainly because of interstitial pneumonia (4 patients) and HFS (one patient).
Conclusions: We demonstrated the efficacy of cooling and oral Dex as primary prevention for PLD-induced HFS. This combination therapy can be considered primary prevention for HFS in ovarian cancer patients on PLD.
Effective treatment of severe stenosis of the carotid and coronary arteries: A case study
Authors: Sugiyama Kayo , Ato Fuminori , Watanuki Hirotaka , Tochii Masato , Miyachi Shigeru , Matsuyama Katsuhiko 
Published: September 2022

Abstract
It is considered acceptable to conservatively manage coronary artery bypass grafting patients with carotid artery disease without the need for preoperative corrective carotid revascularization. However, in the present case, rapidly progressive stroke symptoms with penumbra shown in the arterial spin labeling, carotid artery stenting was performed successfully.
A New enhanced Fuzzy Support Vector Machine with Pinball Loss
Authors: Verma Ram Nayan , Srivast Rakesh , Subbarao Naidu ,   Dr. Gajendra Pratap Singh1
Published: September 2022

Abstract

Abstract. The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.
Convolution-Based Approach for modeling the Paliperidone Extended Release and Long-Acting Injectable (LAI) PK of Once-, and Three-Monthly Products Administration and for Optimizing the Development of New LAI products
Authors: Gomeni Roberto , Bressolle-Gomeni Françoise 
Published: September 2022

Abstract
The aim of this paper was to develop a new modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and three monthly long-acting injectable products and to compare the performances of this new approach to the traditional modeling strategy.
The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling LAI products and for deploying an effective model-informed drug development process (MIDD).
In particular, the convolution-based modeling can a) facilitate the development of in-vitro/in-vivo correlation, b) be used to identify formulations with optimal in vivo release properties, and c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in-vitro and in-vivo drug release, in-vivo drug release to PK, and PK to PD and biomarker endpoints.
A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in-vivodrug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.
A mathematical modeling framework for deciphering the dynamics of tumour-immune interactions in presence of checkpoint inhibitors
Authors: Sen Pramita , Ghosh Sushmit 
Published: September 2022

Abstract
Targeting checkpoint proteins expressed on surfaces of T cells as a consequence of sustained tumour antigen stimulation leads to reinvigoration of immune responses and durable clinical benefits in cancer patients. However, the therapeutic interventionsusing checkpoint inhibitors like anti-CTLA4 and anti-PD1 produce different outcomes in different individuals as a consequence of complex interactions between tumour antigens, immune cells and immune responses. Here a discrete time mathematical model is proposed for establishing the quantitative underpinningsof the tumour-immune interplay occurring during tumour growth. The mathematical motif succintlydescribes the tumour growth history with time, the dynamics of tumour antigens and effector T cells and emphasizes on the effect of primary immune responses alone and combination of primary and secondary immune responses on tumour regression. The model compares the tumour growth dynamics observed in persons lacking secondary immune responses with that observed in persons with both immune responses. The anti-tumour immune responses comprising the primary and secondary responses are downregulated by the checkpoint proteins like PD1 and CTLA4 upon continuous stimulation by tumour antigens. The anti-PD1 and anti-CTLA4 inhibitors inhibit the checkpoint proteins and re-strengthen the immune responses resulting in sustained tumour growth or elimination of the tumour. The model developed further forecasts the effect of variation in the strength of immune responses on tumour growth dynamics, and the various outcomes observed when the monotherapy or combination therapy is discontinued after a certain period of time. Further, the model predictions are used to explore the synergistic interactions between the inhibitors and report the maximum synergy achievable for the drug combination.
Enhancing chaos in multistability regions of Duffing map for an image encryption algorithm
Authors: Natiq Hayder , Roy Animesh , Banerjee Santo , Misra A. P. , Fataf N. A. A. 
Published: September 2022

Abstract
We investigate the dynamics of a 2D chaotic Duffing map that exhibits coexisting chaotic attractors and periodic orbits with a typical set of system parameters. Such unusual behaviors in low-dimensional maps are unacceptable, especially in the applications of chaos-based cryptography. To this end, the Sine-Cosine chaotification technique is used to propose a modified Duffing map in enhancing its chaos complexity in the multistable regions. Based on the enhanced Duffing map, a new image encryption algorithm is developed with the principles of confusion and diffusion. While in the former, hyperchaotic sequences are generated for scrambling of plain-image pixels, the latter is accomplished by the elliptic curves, S-box, and hyperchaotic sequences. Simulation results and security analysis reveal that the proposed encryption algorithm can effectively encrypt and decrypt various kinds of digital images with high-level security.
SBLMD - ANN – MOPSO based hybrid approach for determining optimum parameter in CNC milling
Authors: Mishra Rohit , Singh Bhagat 
Published: September 2022

Abstract
In the recent decades, researchers have proposed various techniques to mitigate chatter effects in milling operation. Still a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been processed using a modified Spline Based Local Mean Decomposition (SBLMD) technique in order to extract tool chatter features. Further, six artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient Based (CGP and SCG), Quasi-Newton Based (BFGS and OSS) and Levenberg-Marquardt Algorithm (LM) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms, suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, multi-objective particle swarm optimization (MOPSO) has been applied to optimize developed prediction models for obtaining an optimal range of input parameters pertaining to stable milling with higher productivity.