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In recent years, rumours and fake news are spreading widely and very rapidly all over the world. Such circumstances lead to the propagation and production of an inaccurate news article. Also, misinformation and fake news are increased by the user without proper verification. Hence, it is necessary to restrict the spreading of fake information on mass media and to promote confidence all over the world. For this purpose, this paper recognizes the detection of fake news in an effective manner. The proposed methodology in detecting fake news consists of four different phases namely the data pre-processing phase, feature reduction phase, feature extraction phase as well as the classification phase. During data pre-processing, the input data are pre-processed by employing tokenization, stop-words deletion as well as stemming. In the second phase, the features are reduced by employing PPCA to enhance accuracy. Then the extracted feature is provided to the classification phase where LSTM-LF algorithm is utilized to classify the news as fake or real optimally. Furthermore, this paper utilizes four different datasets namely the Buzzfeed dataset, GossipCop dataset, ISOT dataset as well as Politifact dataset for evaluation. The performance evaluation and the comparative analysis are conducted and the analysis reveals that the proposed approach provides better performances when compared to other fake detection-based approaches.

Constructing a teaching model that meets the professional requirements and market demand has become an urgent task of teaching reform and innovation breakthrough. This paper proposes a PPCA teaching model for public relations based on the current and potential demand for public relations talent in the digital media era. Based on the proposed teaching model, the teaching practice is carried out at O University of H City. The research data was collected through questionnaires, and then the experimental data was statistically analyzed using two independent samples of t-tests and regression analysis to verify the effectiveness of the proposed model. The results show that the evaluation of the learning process based on the PPCA teaching model is between “good” and “excellent”, and the class that applies this model improves 3.64% more than the class that applies the traditional model in terms of the overall grade, learning attitude and interest, class participation, learning ability, and ability to acquire knowledge. Classes applying this model have higher scores in terms of overall achievement, learning attitude and interest, class participation, learning ability, and ability to acquire knowledge than classes in the traditional model by 3.646-17.856 points, which is superior to some extent. Meanwhile, the student’s active learning initiative (0.147) has the greatest influence on the teaching effectiveness of the PPCA teaching model in public relations. The study provides data that supports the innovation and practice of teaching public relations studies.

The ubiquitous distribution of lysosomes and their heterogeneous protein composition reflects the versatility of these organelles in maintaining cell homeostasis and their importance in tissue differentiation and remodeling. In lysosomes, the degradation of complex, macromolecular substrates requires the synergistic action of multiple hydrolases that usually work in a stepwise fashion. This catalytic machinery explains the existence of lysosomal enzyme complexes that can be dynamically assembled and disassembled to efficiently and quickly adapt to the pool of substrates to be processed or degraded, adding extra tiers to the regulation of the individual protein components. An example of such a complex is the one composed of three hydrolases that are ubiquitously but differentially expressed: the serine carboxypeptidase, protective protein/cathepsin A (PPCA), the sialidase, neuraminidase-1 (NEU1), and the glycosidase β-galactosidase (β-GAL). Next to this ‘core’ complex, the existence of sub-complexes, which may contain additional components, and function at the cell surface or extracellularly, suggests as yet unexplored functions of these enzymes. Here we review how studies of basic biological processes in the mouse models of three lysosomal storage disorders, galactosialidosis, sialidosis, and GM1-gangliosidosis, revealed new and unexpected roles for the three respective affected enzymes, Ppca, Neu1, and β-Gal, that go beyond their canonical degradative activities. These findings have broadened our perspective on their functions and may pave the way for the development of new therapies for these lysosomal storage disorders.

Many traffic management and control applications require highly complete and accurate data of traffic flow. However, because of various reasons such as sensor failure or transmission error, it is common that some traffic flow data are lost. As a result, various methods were proposed by using a wide spectrum of techniques to estimate missing traffic data in the last two decades. Generally, these missing data imputation methods can be categorised into three kinds: prediction methods, interpolation methods and statistical learning methods. To assess their performance, these methods are compared from different aspects in this paper, including reconstruction errors, statistical behaviours and running speeds. Results show that statistical learning methods are more effective than the other two kinds of imputation methods when data of a single detector is utilised. Among various methods, the probabilistic principal component analysis (PPCA) yields best performance in all aspects. Numerical tests demonstrate that PPCA can be used to impute data online before making further analysis (e.g. make traffic prediction) and is robust to weather changes.

Premise The distributions of plant clades are shaped by abiotic and biotic factors as well as historical aspects such as center of origin. Dispersals between distant areas may lead to niche evolution when lineages are established in new environments. Alternatively, dispersing lineages may exhibit niche conservatism, moving between areas with similar environmental conditions. Here we test these contrasting hypotheses in the Datureae clade (Solanaceae). Methods We used maximum likelihood methods to estimate the ancestral range of Datureae along with the history of biogeographic events. We then characterized the niche of each taxon using climatic and soil variables and tested for shifts in environmental niche optima. Finally, we examined how these shifts relate to the niche breadth of taxa and clades within Datureae and the degree of overlap between them. Results Datureae originated in the Andes and subsequently expanded its range to North America and non‐Andean regions of South America. The ancestral niche, and that of most Datura and Trompettia species, is dry, while Brugmansia species likely shifted toward a more mesic environment. Nonetheless, most Datureae present moderate to high overlap in niche breadth today. Conclusions The expansion of Datureae into North America was associated with niche conservatism, with dispersal into similarly dry areas as occupied by the ancestral lineage. Subsequent niche evolution, including the apparent shift to a mesic niche in Brugmansia, diversified the range of habitats occupied by species in the tribe Datureae but also led to significant niche overlap among the three genera.

There are significant differences in the deformation patterns of different parts of arch dams, and there is a common situation of periodic data loss. To accurately analyze the deformation behavior of arch dams, this paper proposes a safety warning and anomaly diagnosis method for arch dam deformation based on the separation of environmental variable effects in different partitions and a knowledge-driven approach. This method combines various techniques such as an optimized ISODATA clustering method, probabilistic principal component analysis (PPCA), square prediction error (SPE) norm control chart, and contribution chart. By defining data forms and rules, existing engineering specifications and experience are transformed into “knowledge” and applied to the operation and management of arch dams, achieving accurate monitoring of arch dam deformation status and timely diagnosis of outliers. Through monitoring data verification of horizontal displacement in a certain arch dam partition, the results show that this method can accurately identify deformation anomalies in the arch dam and effectively separate the influence of environmental variables and noise interference, providing strong support for the safe operation of the arch dam. Accurate deformation monitoring of arch dams is essential for ensuring structural safety and optimizing operational management. However, conventional early warning indicators and empirical models often fail to capture the spatial heterogeneity of deformation and the complex coupling between environmental variables and structural responses. To overcome these limitations, this study proposes a knowledge-driven safety early warning and anomaly diagnosis model for arch dam deformation, based on spatiotemporal clustering and partitioned environmental variable separation. The method integrates the optimized ISODATA clustering algorithm, probabilistic principal component analysis (PPCA), squared prediction error (SPE) control chart, and contribution chart to establish a comprehensive monitoring framework. The optimized ISODATA identifies deformation zones with similar mechanical behavior, PPCA separates environmental influences such as temperature and reservoir level from structural responses, and the SPE and contribution charts quantify abnormal variations and locate potential risk regions. Application of the proposed method to long-term deformation monitoring data demonstrates that the PPCA-based framework effectively separates environmental effects, improves the interpretability of zoned deformation characteristics, and enhances the accuracy and reliability of anomaly identification compared with conventional approaches. These findings indicate that the proposed knowledge-driven model provides a robust and interpretable framework for precise deformation safety evaluation of arch dams.

The synthesis, characterization and applications of sulfamic acid-functionalized magnetic nanoparticles as a magnetically separable nanocatalyst are described. The nanostructure of the catalyst was characterized by FT-IR spectroscopy, thermogravimetric (TGA) analysis, powder X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscope (TEM). Keywords: piperidine-4-carboxylic acid, [Fe.sub.3][O.sub.4]-SA-PPCA, magnetic nanoparticles, recyclability.

The main objective of this research is to propose a novel machine learning based fault detection algorithm for a nonlinear switched continuous system (NSCS) with time delayed measurements. A simulation model has been established through the analysis of the relevant constraints such as estimation errors derived from self-switched time- delayed (TD) Unscented Kalman Filter (UKF). Machine learning techniques, specifically probabilistic principal components analysis (PPCA) and the support vector machine (SVM) have been successfully adopted on the database of the proposed model to detect and isolate the faults. ‘Three tank system’, the commonly used bench mark problem, has been re-employed here to prove the efficiency of the proposed estimation and fault detection algorithm. In this work, it is conjectured that the available measurements are time delayed. Through this study, the efficacy of the proposed time- delayed Unscented Kalman Filter (TD-UKF) and Machine Learning based fault detection have been demonstrated on the afore mentioned benchmark problem. The results indicates that Machine learning based algorithm can detect the fault with more accuracy and less false alarm than other statistical methods. The finding of this research can be used for any type of industrial system to detect the faults with better potential and higher range coverage.

Congenital deficiency of the lysosomal sialidase neuraminidase 1 (NEU1) causes the lysosomal storage disease, sialidosis, characterized by impaired processing/degradation of sialo-glycoproteins and sialo-oligosaccharides, and accumulation of sialylated metabolites in tissues and body fluids. Sialidosis is considered an ultra-rare clinical condition and falls into the category of the so-called orphan diseases, for which no therapy is currently available. In this study we aimed to identify potential therapeutic modalities, targeting primarily patients affected by type I sialidosis, the attenuated form of the disease. We tested the beneficial effects of a recombinant protective protein/cathepsin A (PPCA), the natural chaperone of NEU1, as well as pharmacological and dietary compounds on the residual activity of mutant NEU1 in a cohort of patients’ primary fibroblasts. We observed a small, but consistent increase in NEU1 activity, following administration of all therapeutic agents in most of the fibroblasts tested. Interestingly, dietary supplementation of betaine, a natural amino acid derivative, in mouse models with residual NEU1 activity mimicking type I sialidosis, increased the levels of mutant NEU1 and resolved the oligosacchariduria. Overall these findings suggest that carefully balanced, unconventional dietary compounds in combination with conventional therapeutic approaches may prove to be beneficial for the treatment of sialidosis type I.

Among all the proteins of Periplasmic C type Cytochrome family obtained from cytochrome C7 found in Geobacter sulfurreducens, only the Periplasmic C type Cytochrome A (PPCA) protein can recognize the deoxycholate (DXCA), while its other paralogs do not, as observed from the crystal structures. Though some existing works have used graph-theoretic approaches to realize the 3-D structural properties of proteins, its usage in the rationalisation of the physiochemical behavior of proteins has been very limited. To understand the driving force towards the recognition of DXCA exclusively by PPCA among its paralogs, in this work, we propose two graph theoretic models based on the combinatorial properties, namely, base-pair-type and impact, of the nucleotide bases and the amino acid residues, respectively. Combinatorial analysis of the binding sequences using the proposed base-pair type based graph theoretic model reveals the differential behaviour of PPCA among its other paralogs. Further, to investigate the underlying chemical phenomenon, another graph theoretic model has been developed based on impact. Analysis of the results obtained from impact-based model clearly indicates towards the helix formation of PPCA which is essential for the recognition of DXCA, making PPCA a completely different entity from its paralogs.