Search Results (24)

Contemporary techniques built on deep learning technologies enable precise forecasting of tourism demand, particularly for the relaunch of sustainable tourism following COVID-19. We developed a novel framework to forecast visitor arrivals at tourist attractions in the post-COVID-19 period. To this end, a time-based data partitioning module was first pioneered. The N-BEATS algorithm with multi-step strategies was then imported to build a forecasting system on historical data. We imported visualization of curve fitting, metrics of error measures, wide-range forecasting horizons, different strategies for data segmentations, and the Diebold–Mariano test to verify the robustness of the proposed model. The system was empirically validated using 1604 daily visitor volumes of Jiuzhaigou from 1 January 2020 to 13 May 2024 and 1459 observations of Mount Siguniang from 1 October 2020 to 18 May 2024. The proposed model achieved an average MAPE of 39.60% and MAAPE of 0.32, lower than the five baseline models of SVR, LSTM, ARIMA, SARIMA, and TFT. The results show that the proposed model can accurately capture sudden variations or irregular changes in the observations. The findings highlight the importance of improving destination management and anticipatory planning using the latest time series approaches to achieve sustainable tourist visitation forecasts. Full article

Adeno-associated viruses (AAVs) are small, non-enveloped viruses that package a single-stranded (ss)DNA genome of 4.7 kilobases (kb) within their T = 1 icosahedral capsid. AAVs are replication-deficient viruses that require a helper virus to complete their life cycle. Recombinant (r)AAVs have been utilized as gene delivery vectors for decades in gene therapy applications. So far, six rAAV-based gene medicines have been approved by the US FDA. The 4.7 kb ssDNA genome of AAV encodes nine proteins, including three viral structural/capsid proteins, VP1, VP2, and VP3; four large nonstructural proteins (replication-related proteins), Rep78/68 and Rep52/40; and two small nonstructural proteins. The two nonstructured proteins are viral accessory proteins, namely the assembly associated protein (AAP) and membrane-associated accessory protein (MAAP). Although the accessory proteins are conserved within AAV serotypes, their functions are largely obscure. In this review, we focus on the expression strategy and functional properties of the small nonstructural proteins of AAVs. Full article

Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction. Full article

The expansion of drug resistant parasites sheds a serious concern on several neglected parasitic diseases. Our recent results on cancer led us to envision the use of peptide-alkoxyamines as a highly selective and efficient new drug against schistosome adult worms, the etiological agents of schistosomiasis. Indeed, the peptide tag of the hybrid compounds can be hydrolyzed by worm’s digestive enzymes to afford a highly labile alkoxyamine which homolyzes spontaneously and instantaneously into radicals—which are then used as a drug against Schistosome adult parasites. This approach is nicely summarized as digging their graves with their forks. Several hybrid peptide-alkoxyamines were prepared and clearly showed an activity: two of the tested compounds kill 50% of the parasites in two hours at a concentration of 100 µg/mL. Importantly, the peptide and alkoxyamine fragments that are unable to generate alkyl radicals display no activity. This strong evidence validates the proposed mechanism: a specific activation of the prodrugs by the parasite proteases leading to parasite death through in situ alkyl radical generation. Full article

The emergence and spread of drug-resistant Plasmodium falciparum parasites shed a serious concern on the worldwide control of malaria, the most important tropical disease in terms of mortality and morbidity. This situation has led us to consider the use of peptide-alkoxyamine derivatives as new antiplasmodial prodrugs that could potentially be efficient in the fight against resistant malaria parasites. Indeed, the peptide tag of the prodrug has been designed to be hydrolysed by parasite digestive proteases to afford highly labile alkoxyamines drugs, which spontaneously and instantaneously homolyse into two free radicals, one of which is expected to be active against P. falciparum. Since the parasite enzymes should trigger the production of the active drug in the parasite’s food vacuoles, our approach is summarized as “to dig its grave with its fork”. However, despite promising sub-micromolar IC₅₀ values in the classical chemosensitivity assay, more in-depth tests evidenced that the anti-parasite activity of these compounds could be due to their cytostatic activity rather than a truly anti-parasitic profile, demonstrating that the antiplasmodial activity cannot be based only on measuring antiproliferative activity. It is therefore imperative to distinguish, with appropriate tests, a genuinely parasiticidal activity from a cytostatic activity. Full article

Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings. Full article

Viruses frequently contain overlapping genes, which encode functionally unrelated proteins from the same DNA or RNA region but in different reading frames. Yet, overlapping genes are often overlooked during genome annotation, in particular in DNA viruses. Here we looked for the presence of overlapping genes likely to encode a functional protein in human parvovirus B19 (genus Erythroparvovirus), using an experimentally validated software, Synplot2. Synplot2 detected an open reading frame, X, conserved in all erythroparvoviruses, which overlaps the VP1 capsid gene and is under highly significant selection pressure. In a related virus, human parvovirus 4 (genus Tetraparvovirus), Synplot2 also detected an open reading frame under highly significant selection pressure, ARF1, which overlaps the VP1 gene and is conserved in all tetraparvoviruses. These findings provide compelling evidence that the X and ARF1 proteins must be expressed and functional. X and ARF1 have the exact same location (they overlap the region of the VP1 gene encoding the phospholipase A2 domain), are both in the same frame (+1) with respect to the VP1 frame, and encode proteins with similar predicted properties, including a central transmembrane region. Further studies will be needed to determine whether they have a common origin and similar function. X and ARF1 are probably translated either from a polycistronic mRNA by a non-canonical mechanism, or from an unmapped monocistronic mRNA. Finally, we also discovered proteins predicted to be expressed from a frame overlapping VP1 in other species related to parvovirus B19: porcine parvovirus 2 (Z protein) and bovine parvovirus 3 (X-like protein). Full article

This study successfully achieved high-precision detection of the clean coal ash content in the coal froth flotation domain by integrating deep learning with the likelihood function. Methodologically, a novel data processing and prediction framework was established by combining a deep learning Keras neural network with the likelihood function from probability statistics. The SIFT algorithm was utilized to extract key feature points and descriptors from the images, and keypoint matching and mean-shift clustering algorithms were employed to accurately obtain information on foam motion trajectories and velocities. For parameter optimization, the maximum likelihood estimation was applied to find the optimal parameter estimates of the likelihood function, ensuring enhanced model accuracy. By incorporating the optimized likelihood function parameters into the Keras deep neural network, an efficient prediction model was constructed for the dosage of flotation reagents, froth velocity, and clean coal ash content. The model’s evaluation involved six performance metrics. The experimental results were highly significant, with R² at 0.99997%, RMSE at 0.04458%, MAE at 0.00170%, MAPE at 0.02329%, RRSE at 0.00994%, and MAAPE at 0.00067%. Full article

The use of artemisinin and its derivatives has helped reduce the burden of malaria caused by Plasmodium falciparum. However, artemisinin-resistant parasites are able, in the presence of artemisinins, to stop their cell cycles. This quiescent state can alter the activity of artemisinin partner drugs leading to a secondary drug resistance and thus threatens malaria eradication strategies. Drugs targeting epigenetic mechanisms (namely epidrugs) are emerging as potential antimalarial drugs. Here, we set out to evaluate a selection of various epidrugs for their activity against quiescent parasites, to explore the possibility of using these compounds to counter artemisinin resistance. The 32 chosen epidrugs were first screened for their antiplasmodial activity and selectivity. We then demonstrated, thanks to the specific Quiescent-stage Survival Assay, that four epidrugs targeting both histone methylation or deacetylation as well as DNA methylation decrease the ability of artemisinin-resistant parasites to recover after artemisinin exposure. In the quest for novel antiplasmodial drugs with new modes of action, these results reinforce the therapeutic potential of epidrugs as antiplasmodial drugs especially in the context of artemisinin resistance. Full article

A postulated progressing severe accident scenario has been simulated using MAAP5 code with the focus on ex-vessel cooling of molten corium in the reactor cavity. Various parameters associated with the prediction of molten corium–concrete interaction (MCCI) are identified. Accordingly, a sensitivity analysis is performed to assess the impact of these parameters on the predicted cavity floor erosion depth during this MCCI postulated accident. The sensitivity index of each variable parameter is determined using the Cotter indices method and Sobol′ indices method. At the early stage of the accident, the predicted cavity floor erosion depth is found to be highly sensitive to the downward heat transfer coefficient parameter with Cotter and Sobol′ indices of 94% and 50%, respectively. At the late phase of the accident, however, the cavity floor erosion depth becomes sensitive to melt eruption (Cotter index of 40%), water ingression (Cotter index of 13%), and particulate bed (Cotter index of 15%) parameters alongside the downward heat transfer coefficient (Cotter index of 16%) with the melt eruption parameter becoming dominant. Thus, the sensitivity of the code′s predictions can be minimized by improving the physical models associated with these parameters. Moreover, the sensitivity indices of these parameters can be used by model developers to identify unimportant parameters in a bid to reduce the dimension of the problem with the aim of improving the current predictive capabilities to conduct MCCI-related safety analyses. Full article

Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. Full article

We performed a hydrogen combustion analysis in the Advanced Power Reactor 1400 MWe (APR1400) containment during a severe accident initiated by a small break loss of coolant accident (SBLOCA) which occurred at a lower part of the cold leg using a multi-dimensional hydrogen analysis system (MHAS) to confirm the integrity of the APR1400 containment. The MHAS was developed by combining MAAP, GASFLOW, and COM3D to simulate hydrogen release, distribution and combustion in the containment of a nuclear power plant during the severe accidents in the containment of a nuclear power reactor. The calculated peak pressure due to the flame acceleration by the COM3D, using the GASFLOW results as an initial condition of the hydrogen distribution, was approximately 555 kPa, which is lower than the fracture pressure 1223 kPa of the APR1400 containment. To induce a higher peak pressure resulted from a strong flame acceleration in the containment, we intentionally assumed several things in developing an accident scenario of the SBLOCA. Therefore, we may judge that the integrity of the APR1400 containment can be maintained even though the hydrogen combustion occurs during the severe accident initiated by the SBLOCA. Full article

Black carbon (BC) emissions intensify global warming and are linked to adverse health effects. The International Maritime Organization (IMO) considers the impact of BC emissions from international shipping. A prerequisite for the anticipated limits to BC emissions from marine engines is a reliable measurement method. The three candidate methods (photoacoustic spectroscopy (PAS), laser-induced incandescence (LII), and filter smoke number (FSN)) selected by the IMO were evaluated with extensive ship exhaust matrices obtained by different fuels, engines, and emission control devices. A few instruments targeted for atmospheric measurements were included as well. The BC concentrations were close to each other with the smoke meters (AVL 415S and 415SE), PAS (AVL MSS), LII (Artium-300), MAAP 5012, aethalometers (Magee AE-33 and AE-42), and EC (TOA). In most cases, the standard deviation between instruments was in the range of 5–15% at BC concentrations below 30 mg Sm⁻³. Some differences in the BC concentrations measured with these instruments were potentially related to the ratio of light-absorbing compounds to sulphates or to particle sizes and morphologies. In addition, calibrations, sampling, and correction of thermophoretic loss of BC explained differences in the BC results. However, overall differences in the BC results obtained with three candidate methods selected by the IMO were low despite challenging exhaust compositions from marine diesel engines. Findings will inform decision making on BC emission control from marine engines. Full article

Aggression is highly prevalent in borderline personality disorder (BPD). Previous studies have identified specific biobehavioral mechanisms underlying aggression in BPD, threat sensitivity being among them. We composited the mechanism-based anti-aggression psychotherapy (MAAP) in order to target these specific mechanisms, and MAAP was found to be superior to non-specific supportive psychotherapy (NSSP) in reducing aggressive behavior. In the present study, we investigated whether underlying brain mechanisms expected to be involved were affected by MAAP. To this end, n = 33 patients with BPD and overt aggressive behavior (n = 20 in MAAP, n = 13 in NSSP) and n = 25 healthy participants took part in a functional magnetic resonance imaging emotional face-matching task before and after treatment, or at a similar time interval for controls. Overt aggressive behavior was assessed using the overt aggression scale, modified. Results showed a decrease in amygdala activation in response to facial stimuli after MAAP, whereas an increase in amygdala activation was found after NSSP. Furthermore, in the MAAP group, connectivity between amygdala and dorsomedial prefrontal cortex increased from pre- to post-treatment compared to the NSSP group. Hence, the results suggest an impact of MAAP on brain mechanisms underlying the salience circuit in response to threat cues. Full article