In this study, 24 h quantitative precipitation forecasts (QPFs) by a cloud-resolving model (with a grid spacing of 2.5 km) on days 1–3 for 29 typhoons in six seasons of 2010–2015 in Taiwan were ...examined using categorical scores and rain gauge data. The study represents an update from a previous study for 2010–2012, in order to produce more stable and robust statistics toward the high thresholds (typically with fewer sample points), which is our main focus of interest. This is important to better understand the model’s ability to predict such high-impact typhoon rainfall events. The overall threat scores (TS, defined as the fraction among all verification points that are correctly predicted to reach a given threshold to all points that are either observed or predicted to reach that threshold, or both) were 0.28 and 0.18 on day 1 (0–24 h) QPFs, 0.25 and 0.16 on day 2 (24–48 h) QPFs, and 0.15 and 0.08 on day 3 (48–72 h) QPFs at 350 mm and 500 mm, respectively, showing improvements over 5 km models. Moreover, as found previously, a strong dependence of higher TSs for larger rainfall events also existed, and the corresponding TSs at 350 and 500 mm for the top 5% of events were 0.39 and 0.25 on day 1, 0.38 and 0.21 on day 2, and 0.25 and 0.12 on day 3. Thus, for the top typhoon rainfall events that have the highest potential for hazards, the model exhibits an even higher ability for QPFs based on categorical scores. Furthermore, it is shown that the model has little tendency to overpredict or underpredict rainfall for all groups of events with different rainfall magnitude across all thresholds, except for some tendency to under-forecast for the largest event group on day 3. Some issues associated with categorical statistics to be aware of are also demonstrated and discussed.
The emergence of zebrafish Danio rerio as a versatile model organism provides the unique opportunity to monitor the functions of glycosylation throughout vertebrate embryogenesis, providing insights ...into human diseases caused by glycosylation defects. Using a combination of chemical modifications, enzymatic digestion and mass spectrometry analyses, we establish here the precise glycomic profiles of eight individual zebrafish organs and demonstrate that the protein glycosylation and glycosphingolipid expression patterns exhibits exquisite specificity. Concomitant expression screening of a wide array of enzymes involved in the synthesis and transfer of sialic acids shows that the presence of organ-specific sialylation motifs correlates with the localized activity of the corresponding glycan biosynthesis pathways. These findings provide a basis for the rational design of zebrafish lines expressing desired glycosylation profiles.
Chemical compositions of atmospheric fine particles like PM2.5 prove harmful to human health, particularly to cardiopulmonary functions. Multifaceted health effects of PM2.5 have raised broader, ...stronger concerns in recent years, calling for comprehensive environmental health-risk assessments to offer new insights into air-pollution control. However, there have been few studies adopting local air-quality-monitoring datasets or local coefficients related to PM2.5 health-risk assessment. This study aims to assess health effects caused by PM2.5 concentrations and metal toxicity using epidemiological and toxicological methods based on long-term (2007–2017) hourly monitoring datasets of PM2.5 concentrations in four cities of Taiwan. The results indicated that (1) PM2.5 concentrations and hazardous substances varied substantially from region to region, (2) PM2.5 concentrations significantly decreased after 2013, which benefited mainly from two actions against air pollution, i.e., implementing air-pollution-control strategies and raising air-quality standards for certain emission sources, and (3) under the condition of low PM2.5 concentrations, high health risks occurred in eastern Taiwan on account of toxic substances adsorbed on PM2.5 surface. It appears that under the condition of low PM2.5 concentrations, the results of epidemiological and toxicological health-risk assessments may not agree with each other. This raises a warning that air-pollution control needs to consider toxic substances adsorbed in PM2.5 and region-oriented control strategies are desirable. We hope that our findings and the proposed transferable methodology can call on domestic and foreign authorities to review current air-pollution-control policies with an outlook on the toxicity of PM2.5.
Timely regional air quality forecasting in a city is crucial and beneficial for supporting environmental management decisions as well as averting serious accidents caused by air pollution. Artificial ...Intelligence-based models have been widely used in air quality forecasting. The Shallow Multi-output Long Short-Term Memory (SM-LSTM) model is suitable for regional multi-step-ahead air quality forecasting, while it commonly encounters spatio-temporal instabilities and time-lag effects. To overcome these bottlenecks and overfitting issues, this study proposed a Deep Multi-output LSTM (DM-LSTM) neural network model that were incorporated with three deep learning algorithms (i.e., mini-batch gradient descent, dropout neuron and L2 regularization) to configure the model for extracting the key factors of complex spatio-temporal relations as well as reducing error accumulation and propagation in multi-step-ahead air quality forecasting. The proposed DM-LSTM model was evaluated by three time series of PM2.5, PM10, and NOx simultaneously at five air quality monitoring stations in Taipei City of Taiwan. Results indicated that the loss function values (mean-square-error) of the SM-LSTM and DM-LSTM models in the testing stages at horizon t+4 were 0.87 and 0.72, respectively. The Gbench values of the DM-LSTM model in the testing stages for PM2.5, PM10, and NOx reached 0.95 at horizon t+1 and exceeded 0.81 at horizon t+4, respectively. Results demonstrated that the proposed DM-LSTM model incorporated with three deep learning algorithms could significantly improve the spatio-temporal stability and accuracy of regional multi-step-ahead air quality forecasts.
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•Deep learning multi-output LSTM improved regional multi-step-ahead air quality forecasts.•Integrated three deep learning algorithms to configure and train the DM-LSTM model.•The DM-LSTM model overcame instability and overfitting in spatiotemporal forecasting.•The DM-LSTM model extracted heterogeneities from air pollutant-generating mechanisms.
The fine particulate matter (e.g. PM2.5) gains an increasing concern of human health deterioration. Modelling PM2.5 concentrations remains a substantial challenge due to the limited understanding of ...the dynamic processes as well as uncertainties residing in the emission data and their projections. This study proposed a hybrid model (CNN-BP) engaging a Convolutional Neural Network (CNN) and a Back Propagation Neural Network (BPNN) to make accurate PM2.5 forecasts for multiple stations at multiple horizons at the same time. The hourly datasets of six air quality and two meteorological factors collected from 73 air quality monitoring stations in Taiwan during 2017 formed the case study. A total of 639,480 hourly datasets were collected and allocated into training (409,238, 64%), validation (102,346, 16%), and testing (127,896, 20%) stages. The forecasts of PM2.5 concentrations were first characterized as a function of air quality and meteorological variables. Then the proposed CNN-BP approach effectively learned the dominant features of input data and simultaneously produced accurate regional multi-step-ahead PM2.5 forecasts (73 stations; t+1−t+10). The results demonstrate that the proposed CNN-BP model is remarkably superior to the BPNN, the random forest and the long short term memory neural network models owing to its higher forecast accuracy and excellence in creating reliable regional multi-step-ahead PM2.5 forecasts. Besides, the CNN-BP model not only has the power to cope with the curse of dimensionality by adequately handling heterogeneous inputs with relatively large time-lags but also has the capability to explore different PM2.5 mechanisms (local emission and transboundary transmission) for the five regions (R1-R5) and the whole Taiwan. This study shows that multi-site (regional) and multi-horizon forecasting can be achieved by exactly one model (i.e. the proposed CNN-BP model), hitting a new milestone. Therefore, the CNN-BP model can facilitate real-time PM2.5 forecast service and the forecasts can be made publicly available online.
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•A novel approach fuses convolutional and backpropagation neural networks (CNN-BP).•CNN-BP can make PM2.5 forecasts for multi-stations at multi-horizons simultaneously.•CNN-BP improves reliability and accuracy of regional multi-step-ahead PM2.5 forecasts.•CNN-BP explores various PM2.5 mechanisms (local emission & transboundary transmission).•CNN’s unique framework allows a large input lag to perform in-depth feature learning.
The electrochemical nitrogen reduction reaction (NRR) is an attractive process for next‐generation ammonia synthesis; therefore, identifying a suitable catalyst for this reaction is critical. In ...recent years, transition‐metal dichalcogenides (TMDs) and their Janus structures have gained significant attention because of their outstanding catalytic properties. However, the synthesis of Janus TMDs remains challenging, and exposing their active sites is difficult when using a low‐dimensional structure to improve the catalytic activity. To date, relatively little research has been conducted in this area. Herein, emerging Janus WSeS/WSe2 heterostructure nanowalls are systematically explored. These nanowalls are used as a nitrogen fixation catalyst in electrolytes. The nanowalls demonstrate a significant NH3 yield rate and Faradaic efficiency of 13.97 µg h‐mgcat−1 and 35.24% at −0.3 V in 0.1 m HCl, as well as 15.96 µg h‐mgcat−1 and 40.2% in 0.1 M Na2SO4. This study presents an in‐depth analysis of the properties of Janus WSeS/WSe2 heterostructure nanowalls and a conceptual framework for linking TMD‐based catalysts and the NRR.
Herein, emerging Janus WSeS/WSe2 heterostructure nanowalls are systematically explored. These nanowalls are used as a nitrogen fixation catalyst in the electrolytes. The nanowalls exhibit a high NH3 yield rate and Faradaic efficiency of 13.97 µg h‐mgcat−1 and 35.24% at −0.3 V in 0.1 HCl, as well as 15.96 μg h‐mgcat‐1and 40.2% in 0.1 m Na2SO4. This study presents an in‐depth analysis of the properties of Janus WSeS/WSe2 heterostructure nanowalls and a conceptual framework for linking TMD‐based catalysts and the NRR.
Over 90% of children with Autism Spectrum Disorders (ASD) demonstrate atypical sensory behaviors. In fact, hyper- or hyporeactivity to sensory input or unusual interest in sensory aspects of the ...environment is now included in the DSM-5 diagnostic criteria. However, there are children with sensory processing differences who do not meet an ASD diagnosis but do show atypical sensory behaviors to the same or greater degree as ASD children. We previously demonstrated that children with Sensory Processing Disorders (SPD) have impaired white matter microstructure, and that this white matter microstructural pathology correlates with atypical sensory behavior. In this study, we use diffusion tensor imaging (DTI) fiber tractography to evaluate the structural connectivity of specific white matter tracts in boys with ASD (n = 15) and boys with SPD (n = 16), relative to typically developing children (n = 23). We define white matter tracts using probabilistic streamline tractography and assess the strength of tract connectivity using mean fractional anisotropy. Both the SPD and ASD cohorts demonstrate decreased connectivity relative to controls in parieto-occipital tracts involved in sensory perception and multisensory integration. However, the ASD group alone shows impaired connectivity, relative to controls, in temporal tracts thought to subserve social-emotional processing. In addition to these group difference analyses, we take a dimensional approach to assessing the relationship between white matter connectivity and participant function. These correlational analyses reveal significant associations of white matter connectivity with auditory processing, working memory, social skills, and inattention across our three study groups. These findings help elucidate the roles of specific neural circuits in neurodevelopmental disorders, and begin to explore the dimensional relationship between critical cognitive functions and structural connectivity across affected and unaffected children.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background & Aims
Severe cutaneous adverse drug reactions (SCARs) including Stevens‐Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), drug reaction with eosinophilia and systemic symptoms ...(DRESS) and acute generalized exanthematous pustulosis (AGEP) are high‐mortality adverse drug reactions. The risk factors and prognosis of drug‐induced liver injury (DILI) concomitant with SCAR warrant clarification. We aimed to evaluate the characteristics and outcomes of DILI with SCAR.
Methods
We analysed the database of a 10‐year multi‐centre prospective study in Taiwan from 2011 to 2020.
Results
A total of 1415 patients with DILI were enrolled, including 81 cases combined with SJS/TEN, 74 with DRESS, 3 with AGEP and 1257 with pure DILI. Approximated 11.2% of patients had SCAR, of which allopurinol was the leading incriminated drug, followed by sulphonamides and carbamazepine. The SJS/TEN group had the highest mortality (34.6%). Jaundice, acute kidney injury and SJS/TEN were independent risk factors of mortality (odds ratio: 29.54, 4.43 and 4.86, respectively, P < .003). Chronic kidney disease with high‐dose allopurinol also contributed to high mortality (78.9%) in cases of allopurinol‐induced DILI with SCAR. The HLA‐B*5801 was associated with a high risk and mortality of allopurinol‐induced DILI with SCAR. Likewise, the HLA‐B*1502 was closely related to carbamazepine‐induced DILI with SCAR.
Conclusions
DILI patients combined with SCAR are common and have a high mortality in Taiwan. Allopurinol is the leading incriminated drug. Jaundice, acute kidney injury and SJS/TEN are risk factors of mortality. HLA‐B*5801, chronic kidney disease and high drug dosage also contribute to high mortality in allopurinol‐induced DILI with SCAR.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has the potential for rapid transmission in congregate settings. We describe the multidisciplinary response to an outbreak of coronavirus ...disease (COVID-19) in a large homeless shelter in Chicago, Illinois, USA. The response to the outbreak included 4 rounds of mass PCR testing of all staff and residents and subsequent isolation of persons who tested positive for SARS-CoV-2. We further describe the dynamics of the shelter outbreak by fitting a modified susceptible-exposed-infectious-recovered compartmental model incorporating the widespread SARS-CoV-2 testing and isolation measures implemented in this shelter. Our model demonstrates that rapid transmission of COVID-19 in the shelter occurred before the outbreak was detected; rates of transmission declined after widespread testing and isolation measures were put in place. Overall, we demonstrate the feasibility of mass PCR testing and isolation in congregate settings and suggest the necessity of prompt response to suspected COVID-19 outbreaks in homeless shelters.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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