Mastocytosis is a type of myeloid neoplasm characterized by the clonal, neoplastic proliferation of morphologically and immunophenotypically abnormal mast cells that infiltrate one or more organ ...systems. Systemic mastocytosis (SM) is a more aggressive variant of mastocytosis with extracutaneous involvement, which might be associated with multi-organ dysfunction or failure and shortened survival. Over 80% of patients with SM carry the KIT D816V mutation. However, the KIT D816V mutation serves as a weak oncogene and appears to be a late event in the pathogenesis of mastocytosis. The management of SM is highly individualized and was largely palliative for patients without a targeted form of therapy in past decades. Targeted therapy with midostaurin, a multiple kinase inhibitor that inhibits KIT, has demonstrated efficacy in patients with advanced SM. This led to the recent approval of midostaurin by the United States Food and Drug Administration and European Medicines Agency. However, the overall survival of patients treated with midostaurin remains unsatisfactory. The identification of genetic and epigenetic alterations and understanding their interactions and the molecular mechanisms involved in mastocytosis is necessary to develop rationally targeted therapeutic strategies. This review briefly summarizes recent developments in the understanding of SM pathogenesis and potential treatment strategies for patients with SM.
As the epidemic outbreak of 2019 coronavirus disease (COVID-19), general population may experience psychological distress. Evidence has suggested that negative coping styles may be related to ...subsequent mental illness. Therefore, we investigate the general population's psychological distress and coping styles in the early stages of the COVID-19 outbreak. A cross-sectional battery of surveys was conducted from February 1-4, 2020. The Kessler 6 psychological distress scale, the simplified coping style questionnaire and a general information questionnaire were administered on-line to a convenience sample of 1599 in China. A multiple linear regression analysis was performed to identify the influence factors of psychological distress. General population's psychological distress were significant differences based on age, marriage, epidemic contact characteristics, concern with media reports, and perceived impacts of the epidemic outbreak (all p <0.001) except gender (p = 0.316). The population with younger age (F = 102.04), unmarried (t = 15.28), with history of visiting Wuhan in the past month (t = -40.86), with history of epidemics occurring in the community (t = -10.25), more concern with media reports (F = 21.84), perceived more impacts of the epidemic outbreak (changes over living situations, F = 331.71; emotional control, F = 1863.07; epidemic-related dreams, F = 1642.78) and negative coping style (t = 37.41) had higher level of psychological distress. Multivariate analysis found that marriage, epidemic contact characteristics, perceived impacts of the epidemic and coping style were the influence factors of psychological distress (all p <0.001). Epidemic of COVID-19 caused high level of psychological distress. The general mainland Chinese population with unmarried, history of visiting Wuhan in the past month, perceived more impacts of the epidemic and negative coping style had higher level of psychological distress in the early stages of COVID-19 epidemic. Psychological interventions should be implemented early, especially for those general population with such characteristics.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper presents a novel cooperative unmanned surface vehicle-unmanned aerial vehicle (USV-UAV) platform to form a powerful combination, which offers foundations for collaborative task executed by ...the coupled USV-UAV systems. Adjustable buoys and unique carrier deck for the USV are designed to guarantee landing safety and transportation of UAV. The deck of USV is equipped with a series of sensors, and a multiultrasonic joint dynamic positioning algorithm is introduced for resolving the positioning problem of the coupled USV-UAV systems. To fulfill effective guidance for the landing operation of UAV, we design a hierarchical landing guide point generation algorithm to obtain a sequence of guide points. By employing the above sequential guide points, high-quality paths are planned for the UAV. Cooperative dynamic positioning process of the USV-UAV systems is elucidated, and then UAV can achieve landing on the deck of USV steadily. Our cooperative USV-UAV platform is validated by simulation and water experiments.
The yak is one of the most important domestic animals in Tibetan life for providing basic resources such as milk, meat and transportation. Although yak milk production is not elevated, yak milk is ...superior to dairy cow milk in nutrient composition (protein and fat). However, the understanding of the metabolic mechanisms of yak mammary gland tissue during the lactation cycle remains elusive. In this study, GC-MS-based metabolomics was employed to study the metabolic variations in the yak mammary gland during the lactation cycle (pregnancy, lactation and dry period). Twenty-nine metabolites were up or downregulated during the lactation period. Compared to the dry period, during the lactation period the levels of oxalic acid were upregulated, while glycine and uridine were downregulated. Thirty-seven pathways were obtained when the 29 differential metabolites were imported into the KEGG pathway analysis. The most impacted pathways during the lactation cycle were glycine, serine and threonine metabolism; alanine, aspartate and glutamate metabolism; TCA cycle; glyoxylate and dicarboxylate metabolism; and pyrimidine metabolism. Our results provide important insights into the metabolic events involved in yak mammary gland development, lactogenesis and lactation, which can guide further research to improve milk yield and enhance the constituents of yak milk.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The purpose of this study was to investigate the psychological status of the general population in mainland China during the outbreak of coronavirus disease 2019 (COVID-19), and to explore the ...factors influencing psychological distress, in order to provide the basis for further psychological intervention programs.
We administered three questionnaires on-line to a convenience sample of the general population from different regions of mainland China from February 1 to February 4, 2020. We used the Mandarin versions of the six-item Kessler psychological distress scale (K6), the Simplified Coping Style Questionnaire (SCSQ), and the Social Support Rating Scale (SSRS). We also collected demographic data and other information related to the COVID-19 outbreak. Multivariate binary logistic regression analysis was used to identify factors influencing psychological distress.
Of 1607 respondents, 1588 returned valid questionnaires and were included in the analysis. Nearly one quarter (22.8%) had high levels of psychological distress (K6 score ≥ 13). Individuals with higher psychological distress were more likely to be unmarried, spend more than 6 h per day searching for information about COVID-19, more frequently adopt a passive coping style, and report less social support than those with lower psychological distress.
The COVID-19 outbreak in China has a great impact on the mental health status of the general population. Active coping strategies and increased social support are significantly correlated with decreased psychological distress, and may serve as the basis for psychological interventions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•A LSTM deep learning method for transit arrival prediction is evaluated.•The VMD is adopted to model average bus link speed series into several sub-layers.•The LSTM network is adopted as the ...predictor of each sub-layer.•The VMD-LSTM model provides satisfactory bus speed forecasting results.•The method may be useful to improve transportation systems.
Unreliable transit services can negatively impact transit ridership and discourage passengers from regularly choosing public transport. As the most important content of bus service reliability, accurate bus arrival prediction can improve travel efficiency for enabling a reliable and convenient transportation system. Accordingly, this paper proposes a novel deep learning method, i.e. variational mode decomposition long short-term memory (VMD-LSTM), for bus travel speed prediction in urban traffic networks using a forecast of bus arrival information on variable time horizons. The method uses the temporal and spatial patterns of the average bus speed series. The results show that the VMD-LSTM model outperforms other models on forecasting bus link speed series in future time intervals, whereas the artificial neural network model achieves the worst prediction. In conclusion, the VMD-LSTM method can detect irregular peaks of transit samples from a series of temporal or spatial variations and performs better on major and auxiliary corridors.
•Failure mode of large brittle rocks transfers from ductile to brittle mode.•Rocks with small brittleness are damaged thoroughly under high confining pressure.•Vertical propagation of macro crack on ...large brittle rocks is restrained under confined condition.•Mean and mean peak of cutting force tend to be constants with increase of rock brittleness.•The cutting force fluctuates severely for rocks with large brittleness.
Rock brittleness is one of the most important factors to be considered in the design and selection of excavation and mining machineries. In this paper, the influence mechanism of rock brittleness on rock fragmentation and cutting performance is investigated. Rock models with different brittleness are calibrated by changing the bond shear strength to tensile strength ratio (BSTR) in PFC2D. A linear relationship between the BSTR and brittleness index of B10 with a high correlation coefficient is obtained. A series of rock cutting simulations, using PFC2D, are conducted using different cutting depths and confining pressures on rocks with different brittleness. The analysis results demonstrate that rocks with small brittleness are damaged in the ductile failure mode. In contrast, with the increase in cutting depth, the fracture mode of brittle rocks translated from ductile to brittle mode accompanying the macro crack propagation and large chip formation. Under confined conditions, rocks with small brittleness are damaged thoroughly by the synergistic effect of confining pressure and cutting disturbance when the confining pressure/ uniaxial compressive strength (UCS) ratio is 0.6. For rocks with large brittleness, the vertical propagation of macro cracks are restrained under confined conditions. Moreover, the mean cutting force (MCF) and mean peak cutting force (MPCF) increase and tend to be constants with the increase of rock brittleness and cutting depth. In addition, the instability of the cutting force is evaluated by the fluctuation index (FI) and pulse number (PN) in unit displacement. The FI increases with the increase in rock brittleness while the PN decreases, which suggests that the cutting force fluctuates more violently but less frequently during cutting rocks with large brittleness. Lastly, the analysis of specific energy (SE) on the cutting force signal is carried out, and the results show that it is more efficient to cut rocks with large brittleness than that with small brittleness.
In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an essential part of improving machine life, reducing economic losses, and avoiding safety problems caused by ...machine bearing failures. Most existing bearing fault diagnosis methods face challenges in extracting the fault features from raw bearing fault data. Compared with traditional methods for bearing fault characteristics extraction, deep neural networks can automatically extract intrinsic features without expert knowledge. The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers. It is crucial to identify the classifiers which can maximize the CNN feature extraction ability. In this paper, four hybrid models based on CNN were built, and their fault detection accuracy and efficiency were compared. The comparative analysis showed that the random forest (RF) and support vector machine (SVM) could make full use of the CNN feature extraction ability.
Multibody models built in commercial software packages, e.g., ADAMS, can be used for accurate vehicle dynamics, but computational efficiency and numerical stability are very challenging in complex ...driving environments. These issues can be addressed by using data-driven models, owing to their robust generalization and computational speed. In this study, we develop a deep neural network (DNN) based model to predict longitudinal-lateral dynamics of an autonomous vehicle. Dynamic simulations of the autonomous vehicle are performed based on a semirecursive multibody method for data acquisition. The data are used to train and test the DNN model. The DNN inputs include the torque applied on wheels and the vehicle's initial speed that imitates a double lane change maneuver. The DNN outputs include the longitudinal driving distance, the lateral driving distance, the final longitudinal velocities, the final lateral velocities, and the yaw angle. The predicted vehicle states based on the DNN model are compared with the multibody model results. The accuracy of the DNN model is investigated in detail in terms of error functions. The DNN model is verified within the framework of a commercial software package CarSim. The results demonstrate that the DNN model predicts accurate vehicle states in real time. It can be used for real-time simulation and preview control in autonomous vehicles for enhanced transportation safety.
Owing to enhanced thermal characteristics of nanomaterials, multidisciplinary applications of such particles have been utilized in the industrial and engineering processes, chemical systems, solar ...energy, extrusion processes, nuclear systems etc. The aim of current work is to suggests the thermal performances of thixotropic nanofluid with interaction of magnetic force. The suspension of microorganisms in thixotropic nanofluid is assumed. The investigation is further supported with the triple diffusion flow. The motivations for considering the triple diffusion phenomenon are associated to attaining more thermal applications. The flow pattern is subject to novel stagnation point flow. The convective thermal constraints are incorporated. The modeled problem is numerically evaluated by using shooting technique. Different consequences of physical parameters involving the problem are graphically attributed. The insight analysis is presented for proposed problem with different engineering applications. It is claimed that induced magnetic field enhanced due to magnetic parameter while declining results are observed for thixotropic parameter. The heat transfer enhances due to variation of Dufour number. Furthermore, low profile of nanoparticles concentration has been observed for thixotropic parameter and nano-Lewis number.
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